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Predicting cancer outcomes with radiomics and artificial intelligence in radiology


Abstract and Figures

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.
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In the past decade, drastic increasesin
computational power and memory
have enabled the development and
implementation of state- of- the- art artificial
intelligence (AI) techniques for handling
radiology images. We are currently
witnessing increasing enthusiasm in
this field, especially in oncology imaging,
although computerized methods have
been used in radiology since the 1960s1.
Early initiatives did not gain much traction
because they relied on analogue image
acquisition and limited computational
resources. In the 1980s, the advent of digital
imaging methods and improvements in
computational architecture and storage
renewed interest in these computer- aided
detection (CAD) techniques24. The initial
success with AI in breast cancer detection5
paved the way for AI approaches to be used
more broadly in diagnostic tasks such as
tumour classification and cancer detection.
Over the past decade, AI- based diagnostic
tools have been continuously refined, and in
representation (radiomics or deep learning
(DL)) that can be used in AI applications.
We discuss the clinical implications of
AI in radiology with regard to stratifying
patients by disease severity and prognosis,
predicting treatment response and benefit,
identifying unfavourable treatment
outcomes (for example, hyperprogression)8,
distinguishing confounding responses
(such as pseudoprogression)9,10 from true
disease progression, and non- invasively
predicting salient molecular and genotypic
traits. First, we define AI- enabled imaging
biomarkers and their use, contrasting them
with existing biomarkers in oncology.
Wethen focus on the general framework of
AI- enabled imaging biomarkers, discussing
the technical underpinnings of commonly
used methods. We describe AI tools used in
complex decision- making tasks, providing
examples of how these AI indications have
been used for the management of common
cancer types (further summarized in
Supplementary Table 1). Finally, we
conclude by summarizing some of
the challenges and obstacles along the
path towards clinical adoption of these
approaches and by discussing future
implications for oncology practice.
AI- enabled imaging cancer biomarkers
A biomarker is “a defined characteristic
that is measured as an indicator of normal
biological processes, pathogenic processes
or biological responses to an exposureor
intervention, including therapeutic
interventions”11. On the basis of the type
of clinical decisions they can inform on,
biomarkers can be grouped into several
categories12. In oncology, biomarkers
have applications ranging from prevention,
as is the case for biomarkers of cancer
susceptibility or risk, to guiding high- level
decision- making, among which prognostic
and predictive biomarkers are the most
clinically relevant.
A prognostic biomarker conveys
information pertaining to the risk of a
disease- related end point. In oncology,
prognostic biomarkers are used to determine
the risk profile of a patient with cancer
on the basis of tumour characteristics.
This knowledge enables the clinician to
identify patients with poor prognosis
who might be candidates for escalation of
many cases their diagnostic performance has
been shown to match or even surpass that of
human experts in multiple different cancer
types6,7. This success has led to AIapproaches
now being evaluated to aid more complex
decision- making tasks, such as disease
prognostication, prediction of response to
different treatment modalities, recognition
oftreatment- related changes and discovery of
imaging representations of phenotypic (for
example, sex, age or ethnicity) and genotypic
features associated with prognosis.
In this Perspective, we exclusively focus
on radiology AI- enabled biomarkers to
predict disease outcome and response
to treatment, with the ultimate goal of
providing individualized management.
We aim to equip clinicians interested
in state- of- the- art AI approaches for
decision- making in oncology with
knowledge on the current novel tools
being applied to outcome prediction,
how these approaches are developed
and, specifically, the types of image
Predicting cancer outcomes with
radiomics and artificial intelligence
in radiology
KaustavBera , NathanielBraman, AmitGupta , VamsidharVelcheti
and AnantMadabhushi
Abstract | The successful use of artificial intelligence (AI) for diagnostic purposes
has prompted the application of AI- based cancer imaging analysis to address other,
more complex, clinical needs. In this Perspective, we discuss the next generation
of challenges in clinical decision- making that AI tools can solve using radiology
images, such as prognostication of outcome across multiple cancers, prediction of
response to various treatment modalities, discrimination of benign treatment
confounders from true progression, identification of unusual response patterns
and prediction of the mutational and molecular profile of tumours. We describe
the evolution of and opportunities for AI in oncology imaging, focusing on
hand- crafted radiomic approaches and deep learning- derived representations,
with examples of their application for decision support. We also address the
challenges faced on the path to clinical adoption, including data curation and
annotation, interpretability, and regulatory and reimbursement issues. We hope to
demystify AI in radiology for clinicians by helping them to understand its limitations
and challenges, as well as the opportunities it provides as a decision- support tool
in cancer management.
Nature reviews
CliniCal OnCOlOgy
therapy and/or clinical trials11. Conversely,
if pre- emptively identified, patients with
a good prognosis might have favourable
outcomes with de- escalated therapy and
could thus be spared the physiological and
financial toxicities of cancer treatment.
Most prognostic biomarkers currently used
in oncology are molecular assays that rely
on complex multigene signatures, such as
Oncotype DX and MammaPrint in breast
cancer13 and Decipher in prostate cancer14.
These genomic assays are included in the
National Comprehensive Cancer Network
(NCCN) guidelines and are routinely
used in clinical practice; however, they
are prohibitively expensive and require
tumour tissue obtained through an invasive
procedure, thus limiting their availability
and applicability in serial monitoring
throughout treatment.
A predictive biomarker enables clinicians
to make an informed management
choice byidentifying patients who would
benefit from a particular therapeutic
agent. In oncology, a biomarker is
considered to be predictive if the
treatment effect is statistically different
in patients with biomarker- positive
versus negative status. For example, in
breast, gastric and gastro- oesophageal
cancers, among others, HER2 status
serves as a biomarker for predicting the
effectiveness of HER2- targeted therapies,
such as trastuzumab and pertuzumab15.
In non- small- cell lung cancer (NSCLC),
the presence of EGFR exon 19 deletions or
exon 21 mutations serves as a biomarker of
eligibility for treatment with EGFR tyrosine
kinase inhibitors, such as osimertinib
or erlotinib16. Besides being prognostic,
Oncotype DX is also a predictive biomarker
validated in a prospective clinical trial to
determine benefit from chemotherapy in
women with early- stage breast cancer17.
Rapid AI- driven advancements in
computer vision and pattern recognition
tasks have led to the emergence of
AI- enabled imaging biomarkers. These
biomarkers rely on the extraction of
discriminating quantitative representations
from radiology that capture properties of
the tumour phenotype that correlate with
clinical outcomes. Two main categories of
AI- enabled biomarker in radiology exist:
hand- crafted radiomic and DL approaches18
(TABLE1). With hand- crafted radiomics, a
set of representations are predefined by the
AI development team (involving computer
scientists, radiologists and oncologists) that
are composed of feature measurements
with specific algorithmic derivations.
These feature representations are then
fed into a machine learning (ML) model,
which in turn predicts an outcome. Some
commonly used radiomic approaches focus
on the various attributes of the area inside
thetumour (such as shape or texture) as well
as the tumour microenvironment (TME;
such as texture or tumour vasculature)
(TABLE2). Publicly available radiomics
toolkits12,19 enable researchers to apply
hand- crafted radiomic features in their
work without having to develop the feature
pipeline themselves. In DL approaches,
the development team defines a DL neural
network that can be trained using a large
data set to discover new representations that
can be synthesized to predict a particular
outcome. These approaches have unique
strengths and weaknesses, and require
distinct development workflows (FIG.1).
AI- enabled predictive or prognostic
imaging biomarkers can offer certain
advantages over molecular assays. Given
that they are assessed using routine
clinical radiological scans, AI- enabled
imaging biomarkers are non- invasive,
non- tissue- destructive, rapidly analysed,
easily serialized, fairly inexpensive20 and
fully compatible with existing clinical
workflows, similar to AI- enabled pathology
biomarkers21, with the added advantage of
being non- invasive22,23. They additionally
offer the ability to characterize a tumour
over its full 3D volume, avoiding sampling
errors that can occur with biopsy samples
from heterogeneous tumours24, as well as
enabling the detection of changes in the
TME. Owing to these advantages over
molecular testing, another category of
AI- enabled biomarkers that reflect the
genotype of a tumour has been developed
using imaging representations, an approach
known as radiogenomics. Radiogenomic
approaches predictive of tumour mutational
status could potentially become surrogate
non- invasive biomarkers for established
molecular biomarkers and could be applied
in routine imaging. This approach would be
similar to circulating tumour DNA- based
liquid biopsy approaches, which are being
developed as minimally invasive tools
for cancer surveillance25. Suchtests could
also be used serially to detect changes in
the predominant genotype of a tumour
following initiation of treatment, a known
cause of acquired resistance to targeted
therapy26 that cannot be monitored
accurately with invasive molecular testing.
At present, however, radiogenomic
approaches have several limitations,
including difficulty in assembling
Table 1 | Comparison of hand- crafted radiomic and DL strategies for prediction
and prognosis
Characteristic Radiomics DL
Typically require a lesser amount
of annotated training data
Typically requires large image data
sets for training; this requirement can
be reduced with techniques such as
transfer learning and augmentation
Typically predefined; can be
new features targeted around domain
Involves learning novel feature
representations through trainable
convolutional operations based on
discriminating patterns in training
ML models incorporating radiomic
features are trained to predict cancer
outcomes and treatment response
Prediction model can be trained
simultaneously with learning feature
Annotation Usually involves the need for accurate
delineation of tumour boundaries
and other tissues of interest for
feature extraction
Model can be trained with course
localization or even without any
positional information, if given
sufficient training data
Interpretability Predictions can be attributed to
values of individual measurements
included in the ML model
Challenges in determining factors
contributing to predictions, hence
these approaches tend to be
considered ‘black- box’ approaches;
can be coupled with explainability
approaches (such as class activation
maps) post hoc to provide insight into
model decision- making
Typically, model training and
inference is not computationally
expensive given the smaller number
of model parameters and training
data set sizes
Tends to be more expensive
computationally than radiomics;
usually requires one or more graphical
processing units
DL, deep learning; ML, machine learning.
comprehensive data sets containing imaging,
genomics and clinical information as well
as being restricted to retrospective studies,
and thus currently they are limited to
research settings27. Indeed, these techniques
need further optimization and prospective
validation before clinical deployment28.
A framework for AI- derived biomarkers
Two main AI approaches are currently
used to develop AI- enabled biomarkers
inradiology: radiomics and DL (TABLE1).
These approaches can be leveraged
separately or used in combination2931.
Hand- crafted radiomic models
Several radiomic representations have
proved effective in outcome prediction
(FIG.2; TABLE 2). These representations can
be translated into a predictive or prognostic
model; typically a ML model is trained
using a set of features. A common first
step in this process is feature selection,
which involves algorithmically narrowing
down a large pool of explicit features to a
smaller subset of features best suited for
aparticular task. Features can be chosen to
optimize predictive performance32,33, reduce
correlation within a feature set34 or maximize
robustness and stability35. This reduced
feature representation is then fed into a
statistical ML model (for example, a random
forest classifier) to predict clinical outcomes.
Intensity- based measures. In many cases,
image intensity- based values correspond to
some underlying physiological property of
the tissue and can be leveraged in radiomic
approaches. For example, attenuation values
derived from CT scans directly correspond
to tissue density, and these values can be
used to develop a prognostic biomarker of
outcome36 or tumour phenotype37. Similar
physiological measures on 2- deoxy-2-
18F- fluoro- - glucose (FDG)- PET scans,
which enable quantification of tumour
metabolic activity based on positron
emissions from a metabolized radiotracer,
are highly effective in the early prediction
of outcome for patients with several cancer
types and across treatment modalities3842.
Thedistribution of voxel intensity across the
tumour or other regions of interest can be
further characterized with a broader range
of statistical measures (such as standard
deviation, skewness or kurtosis), commonly
referred to as first- order statistics.
Subvisual heterogeneity and texture.
Tumour heterogeneity can be quantified
through radiological imaging using textural
heterogeneity features, which involves
determination of spatial relationships
between image voxel intensities within a
region of interest. Statistical measures, such
as standard deviation, can provide insights
into the variability of an imaging signal,
but do so across an entire region of interest
(inthis case, the whole tumour). By contrast,
texture features quantify the relationship
between voxels and their surroundings as
a function of both distance and intensity.
Accordingly, texture features might be
better suited to detect tissue architecture
heterogeneity on imaging43.
Signal measurements on radiology
typically correspond to some physical
or biological property of a tissue,
and thus a spatial pattern of greater
intensity variation on imaging is usually
reflective of the underlying anatomical
or physiological heterogeneity of the
tissue itself. For example, intensity
interaction features are commonly used
to explore correlative patterns between
intensities of adjacent voxels, such as
grey- level co- occurrence matrix features44.
Other varieties of texture features involve
the application of targeted image filters
to isolate spatial patterns potentially
relevant to patient outcomes. For example,
Laws’ energy measures use filters that target
specific texture patterns, such as speckling
and waves45.
Shape and volumetric features. Measuring
tumour size over the course of treatment
is standard practice in oncology, and is
commonly performed using the Response
Evaluation Criteria in Solid Tumors
(RECIST)46, an algorithm for monitoring
patient response on longitudinal imaging.
A limited number of strictly 2D tumour
measurements are collected and compared
between examinations to assess whether a
tumour is stable, progressing or responding.
However, RECIST measurements can
vary considerably between radiologists47
and the criteria are not well suited for
certain therapeutic scenarios, such as
identifying pseudoprogression following
immunotherapy48 or monitoring response to
systemic therapy in patients with metastatic
cancers47. Shape radiomics, which refers to
any feature characterizing the shape of a
tissue of interest, enables more sophisticated
analysis of the 3D shape and growth of a
tumour, with higher reproducibility than
clinical assessment of radiology images.
Prospective trials have demonstrated that
tumour volume or changes in tumour
volume during the course of treatment
outperform planar RECIST assessment for
longitudinal monitoring in multiple cancer
types49,50. More sophisticated morphological
measurements, such as surface- to- volume
ratio51 and fractal dimensionality52, offer
detailed characterization of aberrations of
tumour shape and growth patterns. Often,
increased tumour shape complexity is
associated with poor outcome5357.
Peritumoural and TME radiomics. Agrowing
body of work has explored the application
of radiomic features beyond thetumour
tocharacterize the surrounding TME.
TMEradiomic approaches often involve
other radiomic feature families to characterize
signal properties, such as heterogeneity
within non- tumour stroma. Peritumoural
Table 2 | Overview of hand- crafted radiomic features used in oncology
Class of feature Features Common examples
Intensity- based
measures and
first- order statistics
Direct physical or functional
measures from fully quantitative
modalities, and basic statistical
measures characterizing the
distribution of intensity values
within a region
Mean, median, standard deviation,
skewness and kurtosis of image
intensity values, attenuation values
on CT162,163, maximum standardized
uptake value on FDG- PET38
Heterogeneity and
Features of spatial arrangement
and local heterogeneity of image
intensity values
Grey- level co- occurrence matrix44,
grey- level run length matrix164, local
binary patterns165, Gabor wavelets166,
Laws’ energy measures45
Shape and volume Measure of 2D or 3D tumour
Volume, surface- to- volume ratio,
sphericity, compactness, fractal
Characterization of TME through
application of radiomic features
(such as texture) within the
surrounding TME
Textural heterogeneity of the
peritumoural radius surrounding a
tumour58, stroma168, lymph nodes169,
potential metastatic sites109
Radiomics of tumour
Measurements of the function or
shape of the tumour- associated
Vessel tortuosity and structural
organization78,170,171, kinetic and
textural measures of tumour vessels172
FDG, 2- deoxy-2-18F- fluoro- - glucose; TME, tumour microenvironment.
Nature reviews
CliniCal OnCOlOgy
radiomics, which involves extraction of
texture and statistical features within a radius
of tissue surrounding the tumour, has been
shown to have predictive and prognostic
value across a number of treatment
contexts in breast5860, lung6166, brain67,68,
oesophageal69, gastric70,71 and prostate
cancers72, and head and neck squamous
cell carcinoma (HNSCC)73. The inclusion
of analyses of the peritumoural region
increases the predictive power of radiomic
signatures over intratumoural radiomics
Escalation or de-escalation
of therapy
Prioritization for clinical
Actionable mutations
Molecular assays
Serial updates to molecular
Elucidate biological rationale for
imaging AI-based biomarkers
Outcome prediction
Benefit from therapy
Adverse events
Confounder versus
true progression
Patient receives
multiple radiology
during care
Scans and reports
uploaded to picture
archiving and
A physician identifies a
retrospective cohort for
an AI study based upon
indication, treatment,
outcome information
and/or availability of
imaging data
DICOM imaging data for
cohort are curated and
optionally annotated by
radiologist; oncologists
compile database of clinical
and outcome information
Imaging and clinical
data are anonymized
and securely
transferred to AI
ML model
Tumour vessel
and tumour
Radiomics of
tumour shape
Convolutional layers
Radiomics AI approach
Deep learning AI approach
Fully connected layers
Pooling Flattening
80% response
20% non-response
2D or 3D
image data
Fig. 1 | Workflow for AI-enabled biomarkers in radiology. Typical protocol for developing artificial intelligence (AI) radiology biomarkers using radiomic
and deep learning approaches, and their clinical applications. Both approaches can be applied in the context of cancer outcome prediction and biomarker
discovery for assessment of response to treatment, prognostication and radiogenomics. DICOM, Digital Imaging and Communications in Medicine;
ML, machine learning; OS, overall survival; PFS, progression- free survival; RFS, recurrence- free survival.
alone29,58,59,68,7477. Specialized TMEradiomics
approaches tofocus on tumour-associated
vasculature have also shown increasing
promise and are discussed below.
Radiomics of tumour vascularity. Shape-
based radiomic analysis can also be applied
to quantify structural abnormalities in the
tumour- associated vasculature and effects
of tumour angiogenesis. Vessel tortuosity, a
category of features measuring the abnormal
shape of the tumour- associated vasculature,
has shown promise in the prediction of
response to chemotherapy in patients with
breast cancer78,79 or malignant gliomas50
and response to targeted agents in patients
with breast cancer brain metastases80.
Measurements of vessel tortuosity have
also shown promise for identifying those
patients with NSCLC who are likely to have
hyperprogression when receiving immune-
checkpoint inhibitors (ICIs)65. This atypical
response pattern is characterized by a
paradoxical acceleration of tumour growth
following ICIs and requires immediate
therapy cessation.
DL AI models
DL strategies leverage deep neural networks
for pattern recognition, which typically
comprise a series of trainable nonlinear
operations, known as layers, each of which
transforms input data into a representation
that facilitates pattern recognition.
As more layers apply transformations
to the input data, such data become
increasingly abstracted into a deep- feature
representation. The resulting deep features
can eventually be translated by the final layer
of a network into a desired output, such
as the likelihood of a therapeutic outcome
orthe molecular subtype of a tumour. DL is
a vast, technical and dynamically evolving
field. We provide a brief introduction to
the most frequently encountered topics
inthe context of prediction- based radiology
AI, with a more detailed supplementary
discussion of the types of deep neural
network (Supplementary Box 1), popular
architectures (Supplementary Box 2;
Supplementary Table 2) and strategies for
addressing data limitations (Supplementary
Box 3). All these aspects have been reviewed
Convolutional neural networks used for
outcome prediction. The majority of DL-
enabled biomarker applications in radiology
use convolutional neural networks (CNNs)83
(FIG.3a) to derive predictions from imaging
data. CNNs are a specialized type of neural
network designed to learn spatial patterns
in images and they have received substantial
attention owing to their performance in
diagnostic tasks. In several high- profile
studies, CNN- based models have even
surpassed the performance of expert human
readers in interpreting chest radiography84
and CT85, and digital mammography6,86.
Justas CNNs have been shown to be capable
of learning image features indicative of
malignancy, a growing body of research
has shown that they can stratify patients
according to subtle differences in tumour
properties related to outcome, risk and
molecular profiles (FIG.3a). When trained
with patient outcome data, the convolutional
layers of a CNN can learn to recognize novel
imaging phenotypes reflective of prognosis.
CNNs can be applied to 2D or 3Dinputs,
and can be modified with multiple
inputsfor learning from a combination
of image types, such as multiparametric
or dynamic MRI scans87,88. A substantial
number of CNN architectures can be
chosen from for AI-based biomarker studies
(Supplementary Table 2), and their histories
and strengths are discussed in further detail
in SupplementaryBox 2.
Other neural networks in radiology. Fully
convolutional neural networks (FCNs)89
(FIG.3b) are a type of CNN that produces
image- like outputs. FCNs can be used to
map the boundaries of a tumour within an
image for downstream radiomic analyses
(a process known as segmentation) or
unsupervised feature learning when
data are limited (such as by training a
convolutional autoencoder). Likewise,
fullyconnected networks (FIG.3c) are neural
networks without convolutional layers that
can make predictions from various lists of
measurements, such as radiomic features.
Other varieties of neural network can be
combined with CNNs to process multiple
sets of radiological data collected over time,
enabling longitudinal analysis of imaging
data (for example, for response assessment).
These and other variations are discussed in
greater depth in Supplementary Box 1.
Training DL models. To train a DL model,
neural networks are updated iteratively with
subsets of the training data set known as
batches. For each batch, a neural network
first generates predictions of patient
outcomes based on imaging data. These
predictions are then compared with the
corresponding real treatment outcomes via
a loss function — an equation that measures
the correctness of the network outputs. The
value obtained from the loss function is then
used to update the operations performed by
the network layers (FIG.1), making changes
informed most by samples for which the
network performed poorly. A second set
ofpatient data, known as the tuning data
set, is used to monitor performance while
training and optimizing the configuration
and learning processes of the model before
it is applied to an independent data set,
referred to as the test or external validation
data set.
a b
d e f
Fig. 2 | Examples of the types of radiomic feature used in oncology. a | Grey- level co- occurrence
matrix entropy in a metastatic liver lesion detected by CT. b | Shape of a glioblastoma detected on
gadolinium- enhanced T1- weighted MRI. c | Kinetic measure of contrast enhancement over time in
breast tissue using contrast- enhanced MRI. d | Peritumoural radiomics measuring textural heteroge-
neity in the lung stroma surrounding a non- small- cell carcinoma. e | Shape of breast vasculature and
tumour- associated vessel network detected using contrast- enhanced MRI. f | Enhanced standardized
uptake values on 2- deoxy-2-18F- fluoro- - glucose PET–CT scans showing increased metabolic activity
in a head and neck carcinoma.
Nature reviews
CliniCal OnCOlOgy
Training a neural network typically
requires a substantially larger amount of
data than that required for development
of a radiomic model. All ML models are
defined by a set of parameters, which
are variables that specify all the possible
configurations of the algorithm. Increasing
the number of parameters in a model
expands the range of possible solutions it can
discover, but the quantity of data it requires
to learn effectively will also be greater
relative to simpler models. State- of- the- art
CNN architectures comprise millions
of parameters in order to discover novel
prognostic representations directly from the
original data. By contrast, radiomics restricts
prediction problems to a limited pool of
prespecified features combined within a
statistical model with fewer parameters
(typically dozens to hundreds).
The need for a vast quantity of data
can be especially constraining when
models are trained for outcome prediction,
asetting in which viable patient data
might be more limited than in diagnostic
studies. Fortunately, several strategies
exist for leveraging the benefits of neural
networks despite sparse training data.
Forexample, transfer learning80, in which
amodel trained for one pattern recognition
taskis repurposed to perform a newtask,
is frequently used to achieve strong
CNN performance with substantially
less training data. Further strategies are
available to handle limited or flawed
training data80,90 (SupplementaryBox2;
Risk assessment and response
Prognostic approaches
Lung cancer. Most radiomic approaches
for lung cancer management have focused
on NSCLC. Huang etal.91 were among the
first groups to use texture- based hand-
crafted radiomics to develop a prognostic
nomogram to predict disease- free survival
(DFS) in patients with stage I–II NSCLC.
Interestingly, they showed that first- order
statistical measures inside the tumour
(for example, kurtosis) were indicative
2D or 3D image data
2D or 3D image data
Image or volume
Non-image categorization,
prediction or estimation
a Convolutional neural network
c Fully connected networkb Fully convolutional network
Non-image prediction, categorization
or estimation
Estimated treatment end point (such as
response versus non response)
Score or subgroups to stratify patients
by risk
Clinical categories with distinct
phenotype and/or genotype
Continuous physiological measurement
Convolutional layers
Trainable image operations
that can be stacked to learn
complex visual patterns
Fully connected layers
Translate CNN-extracted
deep features into
non-image output and
deep-feature volume
Fig. 3 | Building blocks and types of neural network commonly applied
to medical imaging data. a | Example of a convolutional neural network
(CNN) model configured for prediction. Input images or volumes are passed
through the CNN layers, which perform operations and translate them into
a target output vector. Convolutional layers are sets of operations that trans-
form imaging data into deep- feature representations. Each filter is passed
over the image and paired with a nonlinear activation function to emphasize
visual patterns of interest for a certain task. As more convolutional layers are
stacked, a CNN can learn more complex visual patterns within an image.
Throughout a CNN classifier, deep features are periodically aggregated
through pooling operations. After processing by convolutional and pooling
layers, deep- feature representations are eventually flattened into a vector.
Next, fully connected layers translate these CNN- derived image features into
a vector that corresponds to a target output. These models can be applied
to the prediction of treatment response, prognostication, classification of
tumour subtypes and biomarkers, and prediction of physiological values.
b | Fully convolutional neural networks are a type of CNN comprising only
convolutional layers that yield image- like outputs, such as a map of a tumour’s
location. c | Fully connected networks can be trained to make predictions
based on non- image data, such as radiomic features and clinical variables.
of tumour heterogeneity and 3- year
DFS, and their combination with routine
clinicopathological data (such as sex and
histological grade) outperformed the
tumour, node, metastasis (TNM) staging
criteria alone (C- index 0.72 (95% CI
0.71–0.73) versus 0.63 (95% CI 0.62–0.64)).
Kamran etal.92 developed a radiomic model
using CT scans from patients with limited-
stage small- cell lung cancer to predict
2- year overall survival (OS), locoregional
recurrence and distant metastases. They
observed that radiomic tumour elongation
on radiomics was strongly associated
with locoregional recurrence (HR 1.10;
P = 0.003) and 2- year OS (HR 1.10; P = 0.03).
Pavicetal.93 developed a radiomic model
using FDG- PET images from patients
with mesothelioma to stratify them on
the basis of progression- free survival
(PFS) and OS. The feature with the best
discriminative power was long- run high-
grey- level emphasis, which reflects the
intratumoural heterogeneity of standardized
uptake values (SUVs) on PET scans.
The C- index for PFS was 0.66 (95%CI
0.57–0.78). However, a radiomic model
developed using CT scans from the same
patients had no discriminative power for
outcome prediction93. This study is worth
highlighting because the investigators
applied novel radiomic approaches on
state- of- the- art FDG- PET and CT scans
toprognosticate outcome in mesothelioma,
a rare cancertype.
In the DL domain, Hosny etal.94
trained a 3D CNN to predict 2- year OS
following radiotherapy using CT data and
then adapted the model to predict OS
following surgery with an area under the
curve (AUC) of 0.71 (95% CI 0.60–0.82)
via transfer learning. The study was
unique for several reasons: first, the
researchers used seven independent data
sets involving ~1,200 patients from five
different institutions; second, genomic
association studies revealed correlations
of the DL feature representations with
cell cycle and transcriptional processes,
providing a biological interpretation; and
third, DL features from the area immediately
surrounding the tumour had the highest
prognostic signal.
Breast cancer. Park etal.95 trained an
elastic net survival model to combine radiomic
intensity, texture and morphology features
derived from preoperative MRI scans of
patients with invasive breast cancer into
a radiomics- derived prognostic score;
higher scores were significantly associated
with worse DFS in the testing data set
(P = 0.036). The investigators not only
created a radiomic method for breast
cancer prognostication but also developed
a nomogram combining radiomics and
clinicopathological features for integrated
DFS estimation that performed better than
scores based on each class of feature alone.
Wu etal.96 identified subregions of the
intratumoural environment corresponding
to different levels of perfusion on contrast-
enhanced MRI and quantified interactions
between these subregions through network
analysis. A radiomic signature indicative of
the abundance and distribution of poorly
perfused areas was predictive of recurrence-
free survival (RFS) on multivariable
analysis, adjusting for clinical variables
such as age, volume, receptor status and
pathological response. Interestingly,
tumours with unfavourable prognosis had
a higher proportion of poorly perfused
regions on breast MRI scans than indolent
tumours. Another group97 developed
radiomic signatures using dynamic contrast-
enhanced (DCE) MRI scans from patients
with early- stage breast cancer enrolled on
a completed clinical trial. The developed
signatures independently predicted axillary
lymph node metastasis and 3- year DFS.
These investigators extracted radiomic
features from not only intratumoural and
peritumoural regions but also sentinel
and non- sentinel axillary lymph nodes.
The study revealed that radiomic features
of axillary lymph nodes were equivalent
in prognostic performance to those
from tumour radiomic features alone or
combined with those from lymph nodes.
Chitaliaetal.98 used imaging and outcome
data from patients involved in a completed
clinical trial to develop an imaging
phenotype through clustering of radiomic
features on pretreatment DCE- MRI
scans. They found three phenotypes with
significant variation in image heterogeneity
(P < 0.01) that enabled stratification in
subgroups with significant differences
in10-year RFS (P < 0.05). The signature
was also successfully validated on a publicly
available data set. These researchers showed
that AI can uncover potential intrinsic
imaging phenotypes, corresponding to
different degrees of tumour heterogeneity,
which in turn might be associated with
histologically poorly differentiated tumours
and higher mitotic grades. Drukker etal.99
used a long short- term memory DL model
developed from radiomic features related
to the kinetics of contrast enhancement
from dynamic breast MRI scans performed
throughout neoadjuvant chemotherapy,
which predicted 2- year RFS with a C- index
of 0.80. The study was unique in using
a recurrent neural network (RNN), a
specialized category of DL network, which
integrates and learns using features derived
from images across different time points.
Brain cancer. Most of the radiology
AI research in brain cancer focuses on
glioblastoma, one of the brain tumour
types associated with substantially worse
outcomes. Kickingereder etal.100 used a
hand- crafted radiomic model incorporating
volume, shape and texture features from
multiparametric MRI scans and used a
supervised principal component analysis
to predict PFS (HR 2.43; P = 0.002) and
OS (HR 4.33; P < 0.001) in patients with
glioblastoma. An interesting finding of
this analysis was that all radiomic features
selected for the model were exclusively from
the fluid- attenuated inversion recovery
(FLAIR) sequence, a common MRI
modality, and included grey- level features
indicative of intratumoural heterogeneity.
Beyond intratumoural features, another
group67 developed a radiomic risk score
using 25 texture and entropy features from
both within and outside the tumour, and
integrated these features with molecular
information that included IDH and MGMT
status to predict PFS in the validation data
set (C- index 0.84; P = 0.03). Additionally,
the radiomic risk score was associated with
biological pathways of cell differentiation,
adhesion and angiogenesis. This study was
one of the first to leverage peritumoural
radiomic features for estimating survival
in patients with glioblastoma and to
comprehensively develop an imaging
biomarker by leveraging hand- crafted
radiomics, clinical attributes and mutational
Lao etal.31 extracted ~98,000 features
from multiparametric MRI (T1- weighted
(T1w), T1 contrast (T1c), T2w and FLAIR
modalities) with a transfer learning approach
using a pretrained CNN to predict OS
(C- index 0.71, 95% CI 0.588–0.932) in
glioblastoma. Following feature selection,
a LASSO Cox regression model including
six of the top DL features enabled accurate
stratification of patients in the validation data
set (HR 5.13, 95% CI 2.03–12.96; P < 0.001)
on the basis of OS. Kickingerederetal.101
developed and validated an automatic neural
network (ANN) for theidentification and
volumetric segmentation of contrast-
enhancing tumours and non- enhancing
T2w signal abnormalities on MRI scans.
The ANN- based model was trained on a
data set of patients from one institution
and validated using two data sets: one
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CliniCal OnCOlOgy
internal and another from a completed
clinical trial (EORTC-26101), in which it
had almost a 25% higher performance in
survival prediction relative to the Response
Assessment in Neuro- Oncology (RANO)
criteria (with hazard ratios of 2.59 (95% CI
1.86–3.60) versus 2.07 (95% CI 1.46–2.92) for
ANN and RANO, respectively). This study
was unique in using a clinical trial data set
for validation of the performance, although
this validation was retrospective. Zhou etal.87
presented a novel neural network approach
incorporating brain multiparametric
MRI data (T1w, T1c, T2w and FLAIR)
projected along three spatial dimensions to
form RGB images for a four- input CNN,
which fused data from these images with
lesion measurements andpatient age.
The model was able to stratify patients
into subgroups with an expected median
OS of 0–10months, 10–15 months and
>15 months with an average accuracy of
0.664 ± 0.061 in tenfold cross- validation.
Prostate cancer. Both DL and hand-
crafted radiomics have been applied to
multiparametric MRI scans obtained after
definitive therapy to predict the risk of
prostate cancer recurrence. Shiradkaretal.102
used texture- based radiomics of
pretreatment multiparametric MRI scans
to predict biochemical recurrence after
radical prostatectomy. These investigators
showed that textural heterogeneity and
gradient orientation radiomic features
derived not only from T2w images, but
also from apparent diffusion coefficient
maps, were strongly associated with cancer
recurrence. Zhang etal.103 developed
an AI model using MRI features as well
as clinical parameters to predict 3- year
biochemical recurrence after radical
prostatectomy through cross- validation. A
support vector machine- based ML classifier,
which integrated several imaging features,
PI- RADS score (a structured reporting
system for evaluating clinically significant
cancer on multiparametric MRI) and
clinicopathological features, predicted
3-year biochemical recurrence with an
AUC of 0.95 (95% CI 0.92–0.98). This
study was unique in integrating parameters
from multiple scales and sources to
build an accurate prognostic biomarker.
Zhongetal.104 used a deep transfer learning-
based model to distinguish indolent from
clinically significant prostate cancer using
multiparametric MRI. In the validation data
set, the model outperformed the standard
PI- RADS v2 score in identifying clinically
significant prostate cancer (AUC of 0.726
versus 0.711).
Other cancer types. Wang etal.105 trained
a prognostic model using a set of 16 deep
features obtained via unsupervised feature
learning with a convolutional autoencoder
(Supplementary Box 1) trained on contrast-
enhanced CT images from patients with
high- grade serous ovarian cancer. This
model accurately predicted 3- year RFS
in two different validation data sets
(with AUCs of 0.77 and 0.83; P < 0.05).
Parmaretal.106 developed a radiomic model
using pretreatment CT scans from patients
with NSCLC or HNSCC. Consensus
clustering was performed to select the top
radiomic features for each tumour type,
predicting OS with C- indexes of 0.61 and
0.63 in NSCLC and HNSCC, respectively.
Interestingly, the NSCLC model had AUCs
of 0.56 and 0.61 for predicting tumour
histology andstage, respectively. The
HNSCC model was even more predictive
of histology (AUC0.80) and moderately
predictive of human papillomavirus status
(AUC 0.58). Zhengetal.107 showed that a
radiomic score that included the top six
texture features relating to architectural
heterogeneity extracted from the arterial
phase of pretreatment abdominal CT scans
from patients with solitary hepatocellular
carcinoma was associated with RFS
(P = 0.004) and OS (P = 0.039). In a radiomic
signature108 using first- order statistics
of molecular profiling and pretreatment
contrast- enhanced CT scans from patients
with stage IV colorectal cancer, skewness
was associated with 5- year OS (P = 0.025).
Inaddition, the mean value of positive
pixels was significantly lower in BRAF-
mutated tumours than in BRAF- wild-type
tumours (P = 0.007). Creasy etal.109
demonstrated that radiomic analysis of the
liver parenchyma on presurgical CT scans
could predict the future development of
hepatic metastases in patients following
resection for colon cancer, with 17% of
254radiomic features distinguishing
between hepatic recurrence,extrahepatic
recurrence and non- recurrence (P < 0.05).
This finding suggests that heterogeneity
measures of healthy organ tissue beyond the
site of primary disease might be reflective
of biology that might provide a more viable
premetastatic niche for invasive tumours110.
In the domain of DL, Peng etal.111
developed an AI model using DL features
extracted from four CNNs and hand- crafted
radiomic features from PET and CT images
of patients with nasopharyngeal carcinoma.
This AI model was combined with relevant
clinicopathological parameters to develop
an integrated nomogram that accurately
predicted DFS in an independent validation
data set. Zhang etal.112 used an AI model
combining features learnt from a CNN
pretrained using CT scans from patients
with NSCLC and hand- crafted radiomic
features on CT scans from patients with
pancreatic ductal adenocarcinoma to predict
2- year OS in the latter group, outperforming
traditional DL or radiomic methods.
Predicting response to therapy
Chemotherapy and chemoradiotherapy.
In patients with NSCLC, tumour stage
usually determines treatment stratification.
Patients with stage IA disease generally
receive surgery alone, whereas those with
stage IB–IIB NSCLC tend to undergo
surgical resection followed by adjuvant
chemotherapy. Combination chemotherapy
with a pemetrexed and platinum doublet
is the standard of care for patients with
stageIII NSCLC without metastases,
although some receive radiotherapy or
neoadjuvant chemoradiotherapy followed
by surgery. In a study involving two different
validation data sets of patients with early-
stage NSCLC64, a radiomic nomogram
incorporating features within and outside
the lung nodule on CT scans predicted
benefit from adjuvant chemotherapy and
was prognostic of 3- year DFS (C- index
0.74, 95% CI 0.72–0.76). The score was
used to stratify patients into three groups
according to risk (high, intermediate or
low). Patients in the high- risk group had
a significant DFS benefit with adjuvant
chemotherapy (P = 0.003 in the validation
data sets), whereas those in the low- risk
group had no such benefit. Analysis of
radiomic, pathology and genomic data
revealed that radiomic score was associated
with the spatial arrangement of tumour-
infiltrating lymphocytes (TILs) on histology
images (P = 0.036) and with biological
pathways related to cellular differentiation
and angiogenesis64. Our group61 showed that
a radiomic model comprising intratumoural
and peritumoural texture features could
predict response to pemetrexed–platinum
chemotherapy (AUC 0.77; P < 0.05) and
was strongly associated with OS in patients
with locally advanced NSCLC (HR 2.35,
95% CI 1.41–3.94). The above authors also
developed a radiomic model62 using non-
contrast CT scans from patients with locally
advanced NSCLC receiving neoadjuvant
chemoradiotherapy followed by surgery
to enable stratification by OS (HR 11.18,
95% CI 3.17–44.1) and predict major
pathological response. Corolleretal.113
used radiomic features from both primary
tumours and lymph nodes from patients
with locally advanced NSCLC to predict
pathological complete response (pCR) to
neoadjuvant chemoradiotherapy before
surgery. Three radiomic features that
describe tumour sphericity and lymph
node homogeneity predicted pCR with
an AUC of 0.67 (P < 0.05), while features
quantifying lymph node homogeneity could
also accurately predict residual disease
(AUC 0.72–0.75; P < 0.05). Wei etal.114
developed and validated a radiomic model
to predict response to platinum- based
chemotherapy using data from patients
included in a completed clinical trial, which
achieved an AUC of 0.79 (P < 0.05) on
cross- validation. Regarding DL approaches,
Xuetal.115 combined a pretrained CNN with
a RNN to analyse longitudinal CT scans of
patients with stage III NSCLC before and
after treatment. The AI method had high
performance in predicting pathological
response (P = 0.016) in a validation data
set, and this performance improved as the
number of scans analysed was increased.
With regard to breast cancer, radiomics
and DL approaches have largely been
focused on predicting response to
neoadjuvant chemotherapy116. In a
large- scale multicentre validation
study117, a multiparametric radiomic
model incorporating features from
contrast- enhanced T1w, T2w MRI and
diffusion- weighted imaging accurately
predicted pCR (AUC 0.79; P < 0.05) in
validation data sets from three institutions.
Mazurowski etal.118 found 20 prognostic
radiomic features on DCE- MRI in
patients with invasive breast cancer
that were significantly associated with
distant RFS. Descriptors of size (with the
highest C- index, 0.77, 95% CI 0.67–0.86),
heterogeneity (C- index 0.64, 95% CI
0.52–0.76) and perfusion (C- index 0.70,
95% CI 0.60–0.80) were found to have
the most predictive value. Cain etal.119
evaluated a predictive radiomic signature
on MRI scans from patients who received
neoadjuvant chemotherapy and found it tobe
highly predictive of pCR (AUC 0.71, 95%CI
0.58–0.83) in patients with breast cancer
subtypes associated with poor outcomes
(triple- negative breast cancer (TNBC) and
HER2+ disease). Interestingly, we were
among the first groups to show that adding
textural radiomics of the peritumoural
region immediately surrounding the tumour
to intratumoural features from pretreatment
MRI scans improves predictions of response
to neoadjuvant chemotherapy (AUC 0.74;
P < 0.05). To date, most studies have
aimed to predict response to neoadjuvant
chemotherapy primarily using dynamic
MRI scans, although Tadayyon etal.120
predicted such responses by demonstrating
significant survival differences between
respondersand non- responders at weeks 1
(P = 0.035) and4 (P = 0.027) using texture
features from breast ultrasonography images
with a cross- validation strategy. Regarding
DL, Haetal.121 trained a CNN to predict
response to neoadjuvant chemotherapy
based on pretreatment MRI scans and
reported an accuracy of 88% in a testing
data set. The pCR rate in the study was
higher in patients with TNBC (36%) or
HER2+ (50%) breast cancer compared with
those with luminal A (18%) subtypes, which
is concordant with population studies122.
These investigators hence provided a
potential way to use non- invasive imaging
even before treatment initiation to select
those patients most likely to respond
toneoadjuvant treatment, in contrast to
current standard- of- care imaging methods,
which use post- treatment serial MRIs to
assess response to therapy.
Nie etal.123 developed a radiomic
signature using T2w MRI scans from
patients with confirmed locally advanced
rectal cancer comprising 30 features from
within the tumour, which significantly
predicted pCR (AUC 0.84; P < 0.05)
following neoadjuvant chemoradiotherapy.
Antunes etal.124 built a radiomic model to
predict pCR in a similar patient population,
showing that it was robust and reproducible
across a validation set comprising patients
from two different institutions (AUC 0.71;
P < 0.05) and was consistent across two
different expert tumour annotations (Dice
Similarity coefficient 73.7 ± 14.1 for gross
tumour volume).
Cha etal.125 compared multiple AI
methods, including hand- crafted radiomics
and CNN- based DL radiomics, to predict
pCR in patients with bladder cancer
using CT scans performed before and
after neoadjuvant chemotherapy. The
hand- crafted model and the DL model
achieved AUCs of 0.77 and 0.73, respectively.
Fang etal.126 developed a MRI radiomic
signature derived from the TME using
sagittal T2w, contrast- enhanced T1w and
apparent diffusion coefficient MRI images
from patients with locally advanced cervical
cancer. This model accurately predicted
RECIST response in patients undergoing
concurrent chemoradiotherapy (AUC 0.80,
95% CI 0.68–0.92).
Jiang etal.127 developed a novel DL
AI biomarker using portal venous phase
contrast- enhanced CT scans to predict DFS
and OS in a training data set of patients
with gastric cancer. The model was then
used to build an integrated nomogram
with clinicopathological features that not
only predicted DFS (C- index 0.85, 95% CI
0.83–0.88) and OS (C- index 0.86, 95% CI
0.84–0.89) but also benefit from adjuvant
chemotherapy, in an extensive independent
validation data set.
Targeted therapy. Our group59 showed
that a combination of peritumoural and
intratumoural radiomic features from
DCE-MRI scans of patients with invasive
HER2+ breast cancer could help to identify
intrinsic molecular cancer subtypes,
providing insights into the immune response
within the peritumoural environment
as well as predicting response to HER2-
targeted therapy. In an exploratory
study, Mehtaetal.128 demonstrated that
pharmacokinetic modelling on baseline
breast dynamic MRI could help to
identify patients with downregulation
of angiogenesis pathways following
bevacizumab treatment, which might
be indicative of response to therapy. In a
preliminary study involving patients with
hormone receptor- positive metastatic breast
cancer treated with CDK4/6 inhibitors, our
group129 showed that a radiomic feature-
derived risk score of liver metastases on CT
scans indicating intratumoural heterogeneity
was prognostic of OS (HR 2.02, 95% CI
1.13–3.61; P = 0.0027) and response to
therapy (AUC 0.68; P < 0.05).
Aerts etal.130 analysed data from a
completed clinical trial of patients with
early- stage NSCLC treated with the EGFR
inhibitor gefitinib. They developed a
radiomic model using pretreatment CT
scans, and found that the Laws’ energy
feature was strongly associated with EGFR
mutation status (AUC 0.67; P = 0.03) and
thus associated with a gefitinib response
Immunotherapy. Sun etal.131 used a
radiomic approach based on CT scans to
estimate the presence of CD8+ TILs and
also to predict response to ICIs across four
solid tumour types (HNSCC, NSCLC,
hepatocellular carcinoma and urothelial
carcinoma). Theymodelled the radiomic
analysis on the completed MOSCATO trial
of ICIs, which collected RNA sequencing
data and tumour biopsy samples. The
radiomic signature was validated using a
data set from The Cancer Genome Atlas
(TCGA) for correlation with CD8 gene
expression, and on two other independent
data sets with baseline imaging dataavailable
for tumour immune phenotype association
and ICI responseprediction, respectively.
In the response prediction validation set,
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CliniCal OnCOlOgy
the radiomic signature was associated
with OS (HR 0.52, 95% CI 0.35–0.79) and
could also accurately predict response to
ICIs (P = 0.025). Ourgroup63 developed a
radiomic model using both pretreatment
and immediate post- treatment (6–8weeks)
CT scans of patients with NSCLC receiving
ICIs. The intratumoural and peritumoural
radiomic models predicted RECIST
response (with AUCs of 0.85 and 0.81,
respectively; P < 0.05) and OS (HR 1.64,
95% CI 1.22–2.21) in two independent
data sets. In an exploration of pathological
associations of radiomic features, we
found that peritumoural texture features
were associated with TIL density on tissue
biopsy samples (P < 0.05). Trebeschietal.132
developed a radiomic biomarker using
contrast- enhanced CTscans of primary
and metastatic lesions in patients with
melanoma or NSCLC receiving ICIs;
the model predicted response to ICIs
with high performance across both
tumour types (P < 0.001). Independent
gene set enrichment analysis of patients
with NSCLC revealed radiogenomic
associations with pathways involved in
mitosis and proliferation132. A unique study
by Yangetal.133 introduced a transformer
network able to integrate clinical
measurements, previous interventions
and radiomic features from imaging scans
over a timeline to predict response before
treatment with anti- PD-1 antibodies, with
an AUC of 0.80 in a cross- validation data
set. This approach is innovative owing to its
potential for analysing longitudinal, real-
world clinical data from multiple modalities
that are not available in fixed orders or
time intervals. Our group65 developed
a radiomic predictor that couldclassify
patients with NSCLC receiving ICIs not
only as responders or non- responders but
also as hyperprogressors. Tunali etal.134
retrospectively developed clinical radiomic
models based on four clinical features
together with radiomic textural features
of patients receiving single- agent or
doublet ICIs in clinical trials. These models
successfully identified hyperprogressors
on cross- validation (with AUCs 0.81–0.84)
using only CT scans performed before ICI
treatment initiation.
Radiogenomic approaches. Wu etal.135
described three imaging subtypes in breast
cancer based on the enhancement profile of
the tumour and surrounding parenchyma
on dynamic MRI and explored the
association of these subtypes with prognosis
and genotype. The subtype characterized
by prominent enhancement in the TME
was associated with the poorest 5- year
RFS and with increasing dysregulation of
certain signalling pathways, including those
involved in angiogenesis and protein export.
In another study136, these authors developed
a radiomic signature to estimate percentage
of stromal TILs in pathology samples
(ρ = 0.40, 95% CI 0.24–0.54) and evaluated
the association of the signature with RFS
(P = 0.0008) in an external validation data
set. This signature enabled stratification
of patients into two subgroups, which
were significantly associated with RFS in
patients with TNBC (P = 0.04), for whom
thepresence of TILs is highly prognostic137.
Rao etal.138 used an unsupervised
hierarchical clustering approach to
identify novel phenotypes defined by
multiparametric MRI features in samples
from a TCGA glioblastoma collection with
available microRNA and mRNA expression
data. They identified such a phenotype using
three features that stratified patients into
two subgroups with a statistically significant
difference in OS (P = 0.0002) and differential
expression of transcripts involved in several
immune- related and metabolic pathways.
DL models developed using CT139 and
PET–CT scans140 efficiently predicted EGFR
mutational status in patients with NSCLC
with AUCs of 0.81, for both CT and PET–CT.
A radiogenomic approach141 predicted
KRAF, NRAS and BRAF mutational
status in patients with colorectal cancer.
Pernickaetal.142 analysed radiomic features
in pretreatment CT scans from patients with
resected stage II–III colon cancer to predict
microsatellite instability (MSI)- positive
status, which is associated with a favourable
prognosis. They observed increased textural
homogeneity in MSI- positive tumours
relative to MSI- negative tumours (AUC 0.79;
specificity 96.9%; sensitivity 92.5%; P < 0.05).
Finally, Liu etal.143 developed a CT- based
radiomic signature to predict the expression
status of the genes encoding E- cadherin,
Ki-67, VEGFR2 and EGFR, in patients with
gastric cancer.
Challenges and opportunities
Data curation and annotation
Obtaining sufficient data to develop an
AI- based model is always a challenge, which
is especially pronounced when developing
predictive and prognostic radiology AI tools.
Data from retrospectively acquired data sets
are often most convenient to aggregate, but
raise challenges related to data purity for
both model training and validation because
predefined inclusion and exclusion criteria
might result in unconscious biases in AI
algorithms144. For example, a requirement
for completion of a treatment regimen
might inadvertently exclude patients
who discontinued that regimen owing to
an exceptionally poor response. Hence,
randomized controlled trials (RCTs) are the
gold standard for modelling and validating
biomarkers. AI- based imaging techniques
depend on the signal- to- noise ratio of
both imaging and outcome data. RCTs
provide unbiased and homogeneous data
with well- curated arms for comparative
experimental analysis. Nevertheless, unlike
retrospective data, accessing these RCT data
sets is time consuming and challenging,
often requiring extensive and lengthy
approvals from pharmaceutical companies
or cancer collaborative organizations.
The difficulty in acquiring unbiased
and homogeneous data sets has revealed
the importance of multi- institutional
collaborations in building large data
sets for training and validation of these
techniques. One of these, The Cancer
Imaging Archive145, convened by the
National Cancer Institute (NCI), is a
publicly available repository of aggregated
and prescreened multi- institutional data
sets. This initiative has also brought to the
forefront the importance of cooperative
organizations in oncology, which in the
USA involves the NCI National Clinical
Trial Network groups (such as SWOG,
ECOG and NRG), and worldwide it involves
the European Oganization for Research
and Treatment of Cancer, the Canadian
Cancer Trial Group and the Japan Clinical
Oncology group, which are responsible
for funding and running RCTs. These
organizations already have a crucial role
in biomarker development given that
data sets from completed cooperative
group- led clinical trials can provide
enough power to validate some radiomic
algorithms, enabling prospective evaluation
in RCTs. Additionally, federated learning
techniques146, which are DL AI techniques
for training models from multi- institutional
data sets without actually exchanging data
but instead by sharing training parameters
and weights, might have a role in large- scale
validation of prognostic AI methods.
Once data are acquired, a key preliminary
step in many radiology AI studies is
annotation, the process of defining the
spatial boundaries within which imaging
analysis should be performed. The level of
detail necessary and intensiveness of the
annotation effort depend on the nature
of the study (FIG.4). Radiomics generally
requires precise delineation of tumour
boundaries or other regions of interest,
enabling the computation of measurements
specific to the tumour, such as shape and
heterogeneity. Annotations can be provided
manually by a radiologist or as the outputs
of another ML model, such as a FCN.
Either way, this step should be handled
thoughtfully owing to the high susceptibility
of some features to variations in spatial
delineation147. Alternatively, DL models
can be trained effectively from coarser
labels, such as the approximate location of a
tumour in a volume, drastically reducing the
effort and expertise required for annotation.
With sufficient data, the need for spatial
localization can even be entirely obviated80.
Standardization and reproducibility
Reproducibility across heterogeneous
acquisition protocols, multiple institutions
and patient populations is one of the primary
challenges that AI imaging techniques
must overcome for clinical deployment.
Most radiomic methods have a sharp drop
in performance metrics from training to
independent validation. Lambin etal.148
proposed a quality score indicative of the
robustness of radiomics studies based on
16 components of the radiomics workflow.
Park etal.149 performed a meta- analysis of
77 studies, findinga mean radiomic quality
score of only 26.1% of the maximum and
identifying some key areas for improvement.
In addition to metrics to quantify robustness,
several approaches incorporate stability
measures to build more reproducible
radiomic models. For example, our group150
developed a radiomic method accounting
for both stability and discriminability, and
applied it to predict disease recurrence
in patients with early- stage NSCLC. In
three multi- institutional data sets, the
radiomic model incorporating stability
was substantially stronger in predicting
recurrence than the conventional
radiomic model, despite both models
havingsimilar performance in the training
data set. Researchers have also used
statistical approaches (such as ComBat
harmonization)151 to correct for batch effects
in reconstruction methods (for example,
radiomic feature differences caused by the
use of multiple different image protocols).
Orlhac etal.152 used ComBat on ‘phantom
images’ on CT scans and found that it
enabled realignment of radiomic feature
distributions from multi- institutional data
sets using different CT protocols. Only
models that are robust and reproducible as
well as discriminative will find use in clinical
practice. For this purpose, multicentre
initiatives, such as the Quantitative Imaging
Network153 and the Image Biomarker
Standardization Initiative154, have developed
standardized and optimized sets of radiomic
features for use in research.
Interpretability is one of the challenges that
AI- enabled biomarkers must overcome to
be broadly adopted. Hand- crafted radiomic
tools can offer some intuitiveness into
how an AI algorithm makes its decision;
for example, vessel tortuosity metrics are
attributable to the physical and biological
properties of the vasculature resulting
from tumour angiogenesis. Additionally,
several of the studies previously discussed
herein have focused on explaining the
biological rationale behind radiomic features
through correlation with computational
pathology features63, radiology–pathology
coregistration58 and analysis of biological
pathways or genomic correlations64,94,131.
Nevertheless, major gaps in knowledge
regarding the biological cause of disease
outcomes and treatment responses are areas
that clearly need additional research.
This problem is further compounded
in the context of CNNs or DL networks,
which even lack the limited interpretability
offered by hand- crafted methods and
instead, focus solely on maximizing
performance155. Many of these so- called
‘black- box’ approaches might be perfectly
viable in the diagnostic setting (for example,
AI tools deployed primarily for triaging
time- sensitive scans); however, when it
comes to AI- enabled imaging biomarkers
for optimizing treatment, the question of
interpretability becomes more paramount
because a biomarker- driven treatment
decision needsan explanation rooted in
Although researchers are currently trying
to develop models to explain black- box
approaches, an essential caveat is why the
original model is needed at all if better
models are available. For example, these
approaches can involve saliency or attention
maps integrated into the model itself94,
indicating the specific area of the image
the signal emanates from. Such models
are trained to localize the prognostic and
predictive signal within an image; however,
the specific information contributing to
a prediction within that region cannot
be readily ascertained and might require
additional post hoc biological correlation.
Hence, some researchers have called for the
development of interpretable models from
the outset155, whereas other investigators
contend that performance compared with
present gold standard should be the most
important metric to determine the usability
of the imaging biomarker156, while others
feel that there is a need to go even beyond
explainable AI157.
Regulatory framework and reimbursement
The pathway for regulatory approval is
a key roadblock in the clinical adoption
of imaging- based AI- enabled prognostic
and predictive tools. One of the principles
for regulatory permission includes the
necessary explanation of how the software
works. Inthe USA, the FDA is working on
simplifying the AI approval mechanisms;
in the meantime, AI tools are classified as
medical devices. The FDA has a three- class
system in place to determine the risk posed
by the device, in which class I devices
are those that require the least regulatory
hurdles before they can be marketed.
Patient level Regional level
Annotation detail
Deep learning Radiomics
Tumour level TME level
Fig. 4 | Different levels of annotation detail in radiomics and deep learning studies. Defining the
region of interest and level of annotation detail in radiomics and deep learning studies. TME, tumour
Nature reviews
CliniCal OnCOlOgy
AI- based devices tend to be categorized
as class II or III. To date, the FDA has not
approved any imaging AI- based prognostic
or predictive tool. Several genomic assays
(such as MammaPrint, a prognostic
multigene assay for breast cancer)158 have
received FDA approval through the 510(k)
pathway for class II devices. These approvals
might set a precedent for prognostic and
predictive AI- enabled imaging biomarkers
in oncology to be pursued via the less
rigorous 510(k) approval process instead
of the more restrictive premarket approval
(PMA) process for class III devices. Akin
to the FDA’s tiered device classification,
European Union regulations involve a
four- tiered risk classification system (A–D)
for medical devices, which includes AI
decision- support tools. Only A, the lowest
tier, does not need oversight from the
regulatory body. Similar policies have been
adopted worldwide to regulate AI- based
medical decision- support tools. In an Action
Plan published in January 2021 (REF.159), the
FDA proposed a ‘predetermined change
control plan’ in premarket submissions for
AI tools. This plan will include the types of
anticipated modification in such submissions
and also how they expect algorithms to
change in a controlled manner that manages
risk to patients. The FDA thus expects AI
device providers to commit to real- world
performance monitoring of these tools and to
be able to evaluate such tools from premarket
development to postmarket performance.
In terms of reimbursement, AI tools
do not currently have dedicated common
procedural technology (CPT) codes
for billing. In the USA, CPT codes are
maintained by the American Medical
Association to standardize billing practices
across the country. For AI tools to be
translated into practice, new CPT codes
must be created, but the tool needs to
be approved by the FDA for clinical use
beforehand. Opting out of FDA approval
and going through the Clinical Laboratory
Improvement Amendments (CLIA)
route, aregulatory pathway for lab- based
diagnostic tests (including prognostic and
predictive genomic assays such as Oncotype
DX) might be an interesting option160;
however, the FDA has put out a statement
indicating that it might also regulate CLIA
tests in the future161.
In this Perspective, we provide an overview
of the present and future of AI in radiology
as a tool to identify new predictive and
prognostic biomarkers for use in clinical
decision- making. We believe that this
article will provide clinicians with a
firm foundation on the emerging field
of AI- enabled response and outcome
prediction. In particular, we hope to
facilitate an understanding of the tools and
practices common in radiology AI, and in
particular of which clinical scenarios they
can be used for. We also expect to contribute
to a greater interest in the development and
adoption of AI- enabled imaging biomarkers.
Just as the digitization of radiology in the
past 50 years completely revolutionized
thefield with increased resolution and wider
availability, the next decade is poised for an
AI- fuelled revolution in radiology — not to
replace radiologists, oncologists or clinicians
in general, but to provide them with a new
arsenal of tools to better guide treatment
and, ultimately, improve patient care.
1,2,6, NathanielBraman1,3,6,
1,2, VamsidharVelcheti4 and
AnantMadabhushi1,5 ✉
1Department of Biomedical Engineering, Case Western
Reserve University, Cleveland, OH, USA.
2Department of Radiology, University Hospitals
Cleveland Medical Center, Cleveland, OH, USA.
3Tempus Labs, Chicago, IL, USA.
4Department of Hematology and Oncology,
NYU Langone Health, New York, NY, USA.
5Louis Stokes Cleveland Veterans Medical Center,
Cleveland, OH, USA.
6These authors contributed equally: Kaustav Bera,
Nathaniel Braman.
e- mail:
Published online xx xx xxxx
1. Giger, M. L., Chan, H.-P. & Boone, J. Anniversary
Paper: History and status of CAD and quantitative
image analysis: the role of medical physics and AAPM.
Med. Phys. 35, 5799–5820 (2008).
2. Giger, M. L., Doi, K. & MacMahon, H. Computerized
detection of lung nodules in digital chest radiographs.
Med. Imaging Proc. 767, 384–387 (1987).
3. Carmody, D. P., Nodine, C. F. & Kundel, H. L. An analysis
of perceptual and cognitive factors in radiographic
interpretation. Perception 9, 339–344 (1980).
4. Kundel, H. L. & Hendee, W. R. The perception of
radiologic image information. Report of an NCI
workshop on April 15-16, 1985. Invest. Radiol. 20,
874–877 (1985).
5. Rao, V. M. etal. How widely Is computer- aided detection
used in screening and diagnostic mammography? J. Am.
Coll. Radiol. 7, 802–805 (2010).
6. McKinney, S. M. etal. International evaluation of an
AI system for breast cancer screening. Nature 577,
89–94 (2020).
7. Bejnordi, B. E. etal. Diagnostic assessment of deep
learning algorithms for detection of lymph node
metastases in women with breast cancer. JAMA 318,
2199–2210 (2017).
8. Frelaut, M., Le Tourneau, C. & Borcoman, E.
Hyperprogression under immunotherapy.
Int. J. Mol. Sci. 20, 2674 (2019).
9. Frelaut, M., du Rusquec, P., de Moura, A.,
Le Tourneau, C. & Borcoman, E. Pseudoprogression
and hyperprogression as new forms of response to
immunotherapy. BioDrugs 34, 463–476 (2020).
10. Cruz, L. C. H., da, Rodriguez, I., Domingues, R. C.,
Gasparetto, E. L. & Sorensen, A. G. Pseudoprogression
and pseudoresponse: imaging challenges in the
assessment of posttreatment glioma. Am. J.
Neuroradiol. 32, 1978–1985 (2011).
11. FDA- NIH Biomarker Working Group. BEST
(Biomarkers, EndpointS, and other Tools) Resource
(FDA, 2016).
12. Griethuysen, J. J. M. V. etal. Computational radiomics
system to decode the radiographic phenotype. Cancer
Res. 77, e104–e107 (2017).
13. Nicolini, A., Ferrari, P. & Duffy, M. J. Prognostic and
predictive biomarkers in breast cancer: past, present
and future. Semin. Cancer Biol. 52, 56–73 (2018).
14. Cucchiara, V. etal. Genomic markers in prostate
cancer decision making. Eur. Urol. 73, 572–582
15. LI, S. G. & LI, L. Targeted therapy in HER2-positive
breast cancer. Biomed. Rep. 1, 499–505 (2013).
Cross- validation
A method of analysing model validity without an
independent validation set on a limited data sample by
dividing the training data into subsets for training and
assessing the performance on the complementary
subset of data. Several methods of cross- validation
include holdout, k- fold or leave- one- out.
Elastic net survival model
Type of Cox proportional hazard model that is used to
calculate hazard ratios, which are a way of evaluating
the strength of the association of a variable (for
example, survival outcomes) with a time point. An
elastic net has the added advantage over a standard
Cox model of adjusting for high dimensional data and
covariates that might be correlated with each other,
while making survival estimations.
Grey- level co- occurrence matrix features
Class of commonly used radiomic features, also known
as Haralick features, which rely on higher- order statistics
to describe the spatial arrangement and apparent
position of the different grey levels present throughout
the analysed image.
Statistical measure to indicate the shape of a probability
distribution in terms of its ‘tailedness’. High kurtosis
means high deviation from the mean.
Laws’ energy measures
Eponymously named after K. I. Laws, this radiomic
feature focuses on measuring variations of energy within
a fixed window size, to calculate a combined texture
energy of the pixels analysed.
Long short- term memory
Type of recurrent neural network that has been
supplemented by the addition of recurrent or ‘forget’
gates, which enables the network to learn by looking
back at propagated errors.
Statistical measure to indicate the apparent distance
between the mean and mode of a distribution.
Skewness = (mean – mode)/standard deviation.
Support vector machine
Supervised machine learning model used to classify
data by constructing hyperplanes and choosing the
hyperplane that has the largest separation between
the two classes of interest.
Tumour- infiltrating lymphocytes
(TILs). Lymphocytes that have invaded the tumour
tissue from the bloodstream. In the past few years,
studies have found TILs to be prognostic of survival and
predictive of treatment benefit in several solid tumour
types, including breast and lung tumours.
16. Chan, B. A. & Hughes, B. G. Targeted therapy for
non-small cell lung cancer: current standards and the
promise of the future. Transl. Lung Cancer Res. 4, 36
17. Sparano, J. A. etal. Adjuvant chemotherapy guided
by a 21-gene expression assay in breast cancer.
N. Engl. J. Med. 379, 111–121 (2018).
18. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning.
Nature 521, 436–444 (2015).
19. Pfaehler, E., Zwanenburg, A., Jong, J. Rde &
Boellaard, R. RaCaT: an open source and easy to use
radiomics calculator tool. PLoS ONE 14, e0212223
20. Verma, V. etal. The rise of radiomics and implications
for oncologic management. J. Natl Cancer Inst. 109,
djx055 (2017).
21. Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V.
& Madabhushi, A. Artificial intelligence in digital
pathology — new tools for diagnosis and precision
oncology. Nat. Rev. Clin. Oncol. 16, 703–715 (2019).
22. Wan, T. etal. A radio- genomics approach for
identifying high risk estrogen receptor- positive breast
cancers on DCE- MRI: preliminary results in predicting
oncotypeDX risk scores. Sci. Rep. 6, 21394 (2016).
23. Li, H. etal. MR imaging radiomics signatures for
predicting the risk of breast cancer recurrence as
given by research versions of mammaprint, oncotype
DX, and PAM50 gene assays. Radiology 281,
382–391 (2016).
24. Cyll, K. etal. Tumour heterogeneity poses a significant
challenge to cancer biomarker research. Br. J. Cancer
117 , 367–375 (2017).
25. Crowley, E., Di Nicolantonio, F., Loupakis, F. & Bardelli, A.
Liquid biopsy: monitoring cancer- genetics in the blood.
Nat. Rev. Clin. Oncol. 10 , 472–484 (2013).
26. Lim, Z.-F. & Ma, P. C. Emerging insights of tumor
heterogeneity and drug resistance mechanisms in lung
cancer targeted therapy. J. Hematol. Oncol. 12, 134
27. Mazurowski, M. A. Radiogenomics: what it is and why
it is important. J. Am. Coll. Radiol. 12, 862–866
28. Bodalal, Z., Trebeschi, S., Nguyen- Kim, T. D. L.,
Schats, W. & Beets- Tan, R. Radiogenomics: bridging
imaging and genomics. Abdom. Radiol. 44,
1960–1984 (2019).
29. Eben, J., Braman, N. & Madabhushi, A. in Medical
Image Computing and Computer Assisted Intervention
Vol. 11767 (eds Shen, D. etal.) 602–610 (Springer,
30. Bizzego, A. etal. Integrating deep and radiomics
features in cancer bioimaging. IEEE Conf. Comput.
Intell. Bioinform. Comput. Biol.
10.1109/CIBCB.2019.8791473 (2019).
31. Lao, J. etal. A deep learning- based radiomics model
for prediction of survival in glioblastoma multiforme.
Sci. Rep. 7, 10353 (2017).
32. ‘Student’. The probable error of a mean. Biometrika 6,
1–25 (1908).
33. Wilcoxon, F. Individual comparisons by ranking
methods. Biometrics Bull. 1, 80–83 (1945).
34. Ding, C. & Peng, H. Minimum redundancy feature
selection from microarray gene expression data.
J. Bioinform. Comput. Biol. 3, 185–205 (2005).
35. Chirra, P. etal. Multisite evaluation of radiomic feature
reproducibility and discriminability for identifying
peripheral zone prostate tumors on MRI. J. Med.
Imaging 6, 024502 (2019).
36. Lee, J. W. & etal. Prognostic significance of CT-
attenuation of tumor- adjacent breast adipose tissue in
breast cancer patients with surgical resection. Cancers
11, 1135 (2019).
37. Eguchi, T. etal. Tumor size and computed tomography
attenuation of pulmonary pure ground- glass nodules
are useful for predicting pathological invasiveness.
PLoS ONE 9, e97867 (2014).
38. Kinahan, P. E. & Fletcher, J. W. PET/CT standardized
uptake values (SUVs) in clinical practice and assessing
response to therapy. Semin. Ultrasound CT MR 31,
496–505 (2010).
39. Kubota, K. From tumor biology to clinical PET: a review
of positron emission tomography (PET) in oncology.
Ann. Nucl. Med. 15, 471–486 (2001).
40. Sheikhbahaei, S. etal. The value of FDG PET/CT
in treatment response assessment, follow- up, and
surveillance of lung cancer. Am. J. Roentgenol. 208,
420–433 (2016).
41. Eckstein, J. M. etal. Primary vs nodal site PET/CT
response as a prognostic marker in oropharyngeal
squamous cell carcinoma treated with intensity-
modulated radiation therapy. Head Neck 42,
2405–2413 (2020).
42. Lin, C. etal. Early 18F- FDG PET for prediction
of prognosis in patients with diffuse large B- cell
lymphoma: SUV- based assessment versus visual
analysis. J. Nucl. Med. 48, 1626–1632 (2007).
43. Prasanna, P., Tiwari, P. & Madabhushi, A. Co- occurrence
of local anisotropic gradient orientations (CoLlAGe):
a new radiomics descriptor. Sci. Rep. 6, 37241 (2016).
44. Haralick, R. M., Shanmugam, K. & Dinstein, I. Textural
features for image classification. IEEE Trans. Syst. Man
Cybern. SMC-3, 610–621 (1973).
45. Laws, K. I. Rapid texture identification. SPIE Proc. (1980).
46. Eisenhauer, E. A. etal. New response evaluation
criteria in solid tumours: revised RECIST guideline
(version 1.1). Eur. J. Cancer 45, 228–247 (2009).
47. Kuhl, C. K. etal. Validity of RECIST version 1.1
for response assessment in metastatic cancer:
a prospective, multireader study. Radiology 290,
349–356 (2018).
48. Nishino, M. Tumor response assessment for precision
cancer therapy: response evaluation criteria in solid
tumors and beyond. Am. Soc. Clin. Oncol. Educ. Book
38, 1019–1029 (2018).
49. Hylton, N. M. etal. Locally advanced breast cancer:
MR imaging for prediction of response to neoadjuvant
chemotherapy–results from ACRIN 6657/I- SPY TRIAL.
Radiology 263, 663–672 (2012).
50. Xiao, J. etal. Tumor volume reduction rate is superior
to RECIST for predicting the pathological response of
rectal cancer treated with neoadjuvant chemoradiation:
results from a prospective study. Oncol. Lett. 9,
2680–2686 (2015).
51. Decazes, P. etal. Tumor fragmentation estimated by
the volume surface ratio of tumors measured on FDG
PET/CT is an independent prognostic factor of diffuse
large B- cell lymphoma. J. Nucl. Med. 59, 1416–1416
52. Jang, K., Russo, C. & Di Ieva, A. Radiomics in gliomas:
clinical implications of computational modeling and
fractal- based analysis. Neuroradiology 62, 771–790
53. Ismail, M. etal. Shape features of the lesion habitat
to differentiate brain tumor progression from
pseudoprogression on routine multiparametric MRI:
a multisite study. Am. J. Neuroradiol. 39, 2187–2193
54. Ghose, S. etal. Prostate shapes on pre- treatment MRI
between prostate cancer patients who do and do not
undergo biochemical recurrence are different:
preliminary findings. Sci. Rep. 7, 1–8 (2017).
55. Grove, O. etal. Quantitative computed tomographic
descriptors associate tumor shape complexity and
intratumor heterogeneity with prognosis in lung
adenocarcinoma. PLoS ONE 10, e0118261 (2015).
56. Prasanna, P. etal. Mass effect deformation
heterogeneity (MEDH) on gadolinium- contrast
T1-weighted MRI is associated with decreased survival
in patients with right cerebral hemisphere glioblastoma:
a feasibility study. Sci. Rep. 9, 1–13 (2019).
57. Antunes, J. etal. in Medical Image Computing
and Computer Assisted Intervention Vol. 11767
(eds Shen, D. etal.) 611–619 (Springer, 2019).
58. Braman, N. M. etal. Intratumoral and peritumoral
radiomics for the pretreatment prediction of
pathological complete response to neoadjuvant
chemotherapy based on breast DCE- MRI. Breast
Cancer Res. 19, 57 (2017).
59. Braman, N. etal. Association of peritumoral radiomics
with tumor biology and pathologic response to
preoperative targeted therapy for HER2 (ERBB2)–
positive breast cancer. JAMA Netw. Open. 2, e192561
60. Jones, E. F. etal. MRI enhancement in stromal tissue
surrounding breast tumors: association with
recurrence free survival following neoadjuvant
chemotherapy. PLoS ONE 8, e61969 (2013).
61. Khorrami, M. etal. Combination of peri- and
intratumoral radiomic features on baseline CT
scans predicts response to chemotherapy in lung
adenocarcinoma. Radiol. Artif. Intell. 1, 180012 (2019).
62. Khorrami, M. etal. Predicting pathologic response
to neoadjuvant chemoradiation in resectable stage III
non- small cell lung cancer patients using computed
tomography radiomic features. Lung Cancer 135, 1–9
63. Khorrami, M. etal. Changes in CT radiomic features
associated with lymphocyte distribution predict overall
survival and response to immunotherapy in non–small
cell lung cancer. Cancer Immunol. Res. 8, 108–119
64. Vaidya, P. etal. CT derived radiomic score for
predicting the added benefit of adjuvant
chemotherapy following surgery in stage I, II resectable
non- small cell lung cancer: a retrospective multicohort
study for outcome prediction. Lancet Digital Health 2,
e116–e128 (2020).
65. Vaidya, P. etal. Novel, non- invasive imaging approach
to identify patients with advanced non- small cell lung
cancer at risk of hyperprogressive disease with
immune checkpoint blockade. J. Immunother. Cancer
8, e001343 (2020).
66. Akinci D’Antonoli, T. etal. CT radiomics signature of
tumor and peritumoral lung parenchyma to predict
nonsmall cell lung cancer postsurgical recurrence risk.
Acad. Radiol. 27, 497–507 (2020).
67. Beig, N. etal. Radiogenomic- based survival risk
stratification of tumor habitat on Gd- T1w MRI is
associated with biological processes in glioblastoma.
Clin. Cancer Res. 26, 1866–1876 (2020).
68. Prasanna, P., Patel, J., Partovi, S., Madabhushi, A.
& Tiwari, P. Radiomic features from the peritumoral
brain parenchyma on treatment- naïve multi- parametric
MR imaging predict long versus short- term survival
in glioblastoma multiforme: preliminary findings.
Eur. Radiol. 27, 4188–4197 (2017).
69. Hu, Y. etal. Assessment of intratumoral and
peritumoral computed tomography radiomics for
predicting pathological complete response to
neoadjuvant chemoradiation in patients with
esophageal squamous cell carcinoma. JAMA Netw.
Open 3, e2015927 (2020).
70. Li, J. etal. Intratumoral and peritumoral radiomics
of contrast- enhanced CT for prediction of disease- free
survival and chemotherapy response in stage II/III
gastric cancer. Front. Oncol. 10 , 552270 (2020).
71. Jiang, Y. etal. Noninvasive imaging evaluation of
tumor immune microenvironment to predict outcomes
in gastric cancer. Ann. Oncol. 31, 760–768 (2020).
72. Algohary, A. etal. Combination of peri- tumoral and
intra- tumoral radiomic features on Bi- Parametric MRI
accurately stratifies prostate cancer risk: a multi- site
study. Cancers 12, 2200 (2020).
73. Keek, S. etal. Computed tomography- derived
radiomic signature of head and neck squamous cell
carcinoma (peri)tumoral tissue for the prediction of
locoregional recurrence and distant metastasis after
concurrent chemo- radiotherapy. PLoS ONE 15,
e0232639 (2020).
74. Shan, Q. etal. CT- based peritumoral radiomics
signatures to predict early recurrence in hepatocellular
carcinoma after curative tumor resection or ablation.
Cancer Imaging 19, 11 (2019).
75. Ding, J. etal. Optimizing the peritumoral region size
in radiomics analysis for sentinel lymph node status
prediction in breast cancer. Acad. Radiol. https:// (2020).
76. Dou, T. H., Coroller, T. P., Griethuysen, J. J. M. V.,
Mak, R. H. & Aerts, H. J. W. L. Peritumoral radiomics
features predict distant metastasis in locally advanced
NSCLC. PLoS ONE 13, e0206108 (2018).
77. Chen, S. etal. Pretreatment prediction of immunoscore
in hepatocellular cancer: a radiomics- based clinical
model based on Gd- EOB-DTPA- enhanced MRI imaging.
Eur. Radiol. 29, 4177–4187 (2019).
78. Braman, N., Prasanna, P., Alilou, M., Beig, N.
& Madabhushi, A. in Medical Image Computing
and Computer Assisted Intervention Vol. 11071
(eds Frangi, A. F., Schnabel, J. A., Davatzikos, C.,
Alberola- López, C.& Fichtinger, G.) 803–811
(Springer, 2018).
79. Bullitt, E. etal. Blood vessel morphologic changes
depicted with MR angiography during treatment of
brain metastases: a feasibility study. Radiology 245,
824–830 (2007).
80. Cheplygina, V., Bruijne, M. D. & Pluim, J. P. W. Not-
so-supervised: a survey of semi- supervised, multi-
instance, and transfer learning in medical image
analysis. Med. Image Anal. 54, 280–296 (2019).
81. Chartrand, G. etal. Deep learning: a primer for
radiologists. RadioGraphics 37, 2113–2131 (2017).
82. Miotto, R., Wang, F., Wang, S., Jiang, X. & Dudley, J. T.
Deep learning for healthcare: review, opportunities and
challenges. Brief. Bioinform. 19, 1236–1246 (2018).
83. LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-
based learning applied to document recognition.
Proc. IEEE 86, 2278–2324 (1998).
84. Rajpurkar, P. etal. Deep learning for chest radiograph
diagnosis: a retrospective comparison of the CheXNeXt
algorithm to practicing radiologists. PLoS Med. 15,
e1002686 (2017).
85. Ardila, D. etal. End- to-end lung cancer screening
with three- dimensional deep learning on low- dose
chest computed tomography. Nat. Med. 25, 954–961
Nature reviews
CliniCal OnCOlOgy
86. Wu, N. etal. Deep neural networks improve
radiologists’ performance in breast cancer screening.
IEEE Trans. Med. Imaging 39, 1184–1194 (2020).
87. Zhou, T. etal. in Medical Image Computing and
Computer Assisted Intervention Vol. 12262
(edsMartel A. L. etal.) 221–231 (Springer, 2020).
88. Braman, N. etal. Deep learning- based prediction of
response to HER2-targeted neoadjuvant chemotherapy
from pre- treatment dynamic breast MRI: a multi-
institutional validation study. Preprint at arXiv https:// (2020).
89. Shelhamer, E., Long, J. & Darrell, T. Fully convolutional
networks for semantic segmentation. IEEE Trans.
Pattern Anal. Mach. Intell. 39, 640–651 (2017).
90. Zhou, Z.-H. A brief introduction to weakly supervised
learning. Natl Sci. Rev. 5, 44–53 (2018).
91. Huang, Y. etal. Radiomics signature: a potential
biomarker for the prediction of disease- free survival
in early- stage (I or II) non — small cell lung cancer.
Radiology 281, 947–957 (2016).
92. Kamran, S. C. etal. The impact of quantitative
CT-based tumor volumetric features on the outcomes
of patients with limited stage small cell lung cancer.
Radiat. Oncol. 15, 14 (2020).
93. Pavic, M. etal. FDG PET versus CT radiomics to
predict outcome in malignant pleural mesothelioma
patients. EJNMMI Res. 10 , 81 (2020).
94. Hosny, A. etal. Deep learning for lung cancer
prognostication: a retrospective multi- cohort
radiomics study. PLoS Med. 15, e1002711 (2018).
95. Park, H. etal. Radiomics signature on magnetic
resonance imaging: association with disease- free
survival in patients with invasive breast cancer.
Clin. Cancer Res. 24, 4705–4714 (2018).
96. Wu, J. etal. Intratumoral spatial heterogeneity
at perfusion MR imaging predicts recurrence- free
survival in locally advanced breast cancer treated with
neoadjuvant chemotherapy. Radiology 288, 26–35
97. Yu, Y. etal. Development and validation of a
preoperative magnetic resonance imaging radiomics-
based signature to predict axillary lymph node
metastasis and disease- free survival in patients with
early- stage breast cancer. JAMA Netw. Open 3,
e2028086 (2020).
98. Chitalia, R. D. etal. Imaging phenotypes of breast
cancer heterogeneity in preoperative breast dynamic
contrast enhanced magnetic resonance imaging
(DCE-MRI) scans predict 10-year recurrence. Clin.
Cancer Res. 26, 862–869 (2020).
99. Drukker, K., Edwards, A., Papaioannou, J. & Giger, M.
Deep learning predicts breast cancer recurrence in
analysis of consecutive MRIs acquired during the
course of neoadjuvant chemotherapy. Proc. SPIE
11314, 1131410 (2020).
100. Kickingereder, P. etal. Radiomic profiling of
glioblastoma: identifying an imaging predictor
of patient survival with improved performance
over established clinical and radiologic risk models.
Radiology 280, 880–889 (2016).
101. Kickingereder, P. etal. Automated quantitative tumour
response assessment of MRI in neuro- oncology with
artificial neural networks: a multicentre, retrospective
study. Lancet Oncol. 20, 728–740 (2019).
102. Shiradkar, R. etal. Radiomic features from pretreatment
biparametric MRI predict prostate cancer biochemical
recurrence: preliminary findings. J. Magn. Reson.
Imaging 48, 1626–1636 (2018).
103. Zhang, Y.-D. etal. An imaging- based approach predicts
clinical outcomes in prostate cancer through a novel
support vector machine classification. Oncotarget 7,
78140–78151 (2016).
104. Zhong, X. etal. Deep transfer learning- based prostate
cancer classification using 3 Tesla multi- parametric
MRI. Abdom. Radiol. 44, 2030–2039 (2019).
105. Wang, S. etal. Deep learning provides a new
computed tomography- based prognostic biomarker
for recurrence prediction in high- grade serous ovarian
cancer. Radiother. Oncol. 132, 171–177 (2019).
106. Parmar, C. etal. Radiomic feature clusters and
Prognostic Signatures specific for Lung and Head
& Neck cancer. Sci. Rep. 5, 11044 (2015).
107. Zheng, B.-H. etal. Radiomics score: a potential
prognostic imaging feature for postoperative survival
of solitary HCC patients. BMC Cancer 18, 1148
108. Negreros- Osuna, A. A. etal. Radiomics texture
features in advanced colorectal cancer: correlation
with BRAF mutation and 5-year overall survival.
Radiology 2, e190084 (2020).
109. Creasy, J. M. etal. Differences in liver parenchyma are
measurable with CT radiomics at initial colon resection
in patients that develop hepatic metastases from
stage II/III colon cancer. Ann. Surg. Oncol. 28,
1982–1989 (2021).
110 . Langley, R. R. & Fidler, I. J. The seed and soil hypothesis
revisited–the role of tumor–stroma interactions in
metastasis to different organs. Int. J. Cancer 128,
2527–2535 (2011).
111. Peng, H. etal. Prognostic value of deep learning PET/
CT- based radiomics: potential role for future individual
induction chemotherapy in advanced nasopharyngeal
carcinoma. Clin. Cancer Res. 25, 4271–4279 (2019).
112 . Zhang, Y. etal. Improving prognostic performance in
resectable pancreatic ductal adenocarcinoma using
radiomics and deep learning features fusion in CT
images. Sci. Rep. 11, 1378 (2021).
113 . Coroller, T. P. etal. Radiomic- based pathological
response prediction from primary tumors and lymph
nodes in NSCLC. J. Thorac. Oncol. 12, 467–476
114 . Wei, H. etal. Application of computed tomography-
based radiomics signature analysis in the prediction
of the response of small cell lung cancer patients
to first-line chemotherapy. Exp. Ther. Med. 17,
3621–3629 (2019).
115 . Xu, Y. etal. Deep learning predicts lung cancer
treatment response from serial medical imaging.
Clin. Cancer Res. 25, 3266–3275 (2019).
116 . Granzier, R. W. Y., van Nijnatten, T. J. A.,
Woodruff, H. C., Smidt, M. L. & Lobbes, M. B. I.
Exploring breast cancer response prediction to
neoadjuvant systemic therapy using MRI- based
radiomics: a systematic review. Eur. J. Radiol. 121,
108736 (2019).
117 . Liu, Z. etal. Radiomics of multi- parametric MRI for
pretreatment prediction of pathological complete
response to neoadjuvant chemotherapy in breast
cancer: a multicenter study. Clin. Cancer Res. 25,
3538–3547 (2019).
118 . Mazurowski, M. A. etal. Association of distant
recurrence- free survival with algorithmically extracted
MRI characteristics in breast cancer. J. Med. Reson.
Imaging 49, e231–e240 (2019).
119 . Cain, E. H. etal. Multivariate machine learning models
for prediction of pathologic response to neoadjuvant
therapy in breast cancer using MRI features: a study
using an independent validation set. Breast Cancer
Res. Treat. 173, 455–463 (2019).
120. Tadayyon, H. etal. A priori prediction of breast
tumour response to chemotherapy using quantitative
ultrasound imaging and artificial neural networks.
Oncotarget 10, 3910–3923 (2019).
121. Ha, R. etal. Prior to initiation of chemotherapy, can
we predict breast tumor response? Deep learning
convolutional neural networks approach using a
breast MRI tumor dataset. J. Digit. Imaging 32,
693–701 (2019).
122. Houssami, N., Macaskill, P., von Minckwitz, G.,
Marinovich, M. L. & Mamounas, E. Meta- analysis
of the association of breast cancer subtype and
pathologic complete response to neoadjuvant
chemotherapy. Eur. J. Cancer 48, 3342–3354
123. Nie, K. etal. Rectal cancer: assessment of neoadjuvant
chemoradiation outcome based on radiomics
of multiparametric MRI. Clin. Cancer Res. 22,
5256–5264 (2016).
124. Antunes, J. T. etal. Radiomic features of primary rectal
cancers on baseline T2-weighted MRI are associated
with pathologic complete response to neoadjuvant
chemoradiation: a multisite study. J. Magn. Reson.
Imaging 52, 1531–1541 (2020).
125. Cha, K. H. etal. Bladder cancer treatment response
assessment in CT using radiomics with deep- learning.
Sci. Rep. 7, 1–12 (2017).
126. Fang, M. etal. Multi- habitat based radiomics for the
prediction of treatment response to concurrent
chemotherapy and radiation therapy in locally
advanced cervical cancer. Front. Oncol. 10, 563
127. Jiang, Y. etal. Development and validation of a
deep learning CT signature to predict survival and
chemotherapy benefit in gastric cancer: a multicenter,
retrospective study. Ann. Surg.
10.1097/SLA.0000000000003778 (2020).
128. Mehta, S. etal. Radiogenomics monitoring in breast
cancer identifies metabolism and immune checkpoints
as early actionable mechanisms of resistance to anti-
angiogenic treatment. EBioMedicine 10, 109–116
129. Kunte, S. etal. Radiomics risk score (RRS) on CT to
predict survival and response to CDK 4/6 inhibitors
in hormone receptor (HR) positive metastatic breast
cancer (MBC). J. Clin. Oncol. 38, e13041–e13041
130. Aerts, H. J. W. L. etal. Defining a radiomic response
phenotype: a pilot study using targeted therapy in
NSCLC. Sci. Rep. 6, 33860 (2016).
131. Sun, R. etal. A radiomics approach to assess tumour-
infiltrating CD8 cells and response to anti- PD-1 or
anti- PD-L1 immunotherapy: an imaging biomarker,
retrospective multicohort study. Lancet Oncol. 19,
1180–1191 (2018).
132. Trebeschi, S. etal. Predicting response to cancer
immunotherapy using noninvasive radiomic biomarkers.
Ann. Oncol. 30, 998–1004 (2019).
133. Yang, J. etal. in Medical Image Computing and
Computer Assisted Intervention Vol. 12262
(eds Martel, A. L. etal.) 211–220 (2020).
134. Tunali, I. etal. Novel clinical and radiomic predictors
of rapid disease progression phenotypes among lung
cancer patients treated with immunotherapy: an early
report. Lung Cancer 129, 75–79 (2019).
135. Wu, J. etal. Unsupervised Clustering of quantitative
image phenotypes reveals breast cancer subtypes
with distinct prognoses and molecular pathways.
Clin. Cancer Res. 23, 3334–3342 (2017).
136. Wu, J. etal. Magnetic resonance imaging and
molecular features associated with tumor- infiltrating
lymphocytes in breast cancer. Breast Cancer Res. 20,
101 (2018).
137. Loi, S. etal. Tumor- infiltrating lymphocytes and
prognosis: a pooled individual patient analysis of
early- stage triple- negative breast cancers. J. Clin.
Oncol. 37, 559–569 (2019).
138. Rao, A. etal. A combinatorial radiographic phenotype
may stratify patient survival and be associated with
invasion and proliferation characteristics in
glioblastoma. J. Neurosurg. 124, 1008–1017 (2016).
139. Wang, S. etal. Predicting EGFR mutation status in
lung adenocarcinoma on computed tomography image
using deep learning. Eur. Respir. J. 53, 1800986
140. Mu, W. etal. Non- invasive decision support for NSCLC
treatment using PET/CT radiomics. Nat. Commun. 11,
5228 (2020).
141. Yang, L. etal. Can CT- based radiomics signature
predict KRAS/NRAS/BRAF mutations in colorectal
cancer? Eur. Radiol. 28, 2058–2067 (2018).
142. Golia Pernicka, J. S. etal. Radiomic- based prediction
of microsatellite instability in colorectal cancer at
initial computed tomography evaluation. Abdom.
Radiol. 44, 3755–3763 (2019).
143. Liu, S. etal. CT textural analysis of gastric cancer:
correlations with immunohistochemical biomarkers.
Sci. Rep. 8, 11844 (2018).
144. Park, S. H. & Han, K. Methodologic guide for
evaluating clinical performance and effect of artificial
intelligence technology for medical diagnosis and
prediction. Radiology 286, 800–809 (2018).
145. Clark, K. etal. The Cancer Imaging Archive (TCIA):
maintaining and operating a public information
repository. J. Digit. Imaging 26, 1045–1057 (2013).
146. Sheller, M. J. etal. Federated learning in medicine:
facilitating multi- institutional collaborations without
sharing patient data. Sci. Rep. 10, 12598 (2020).
147. Traverso, A., Wee, L., Dekker, A. & Gillies, R.
Repeatability and reproducibility of radiomic features:
a systematic review. Int. J. Radiat. Oncol. Biol. Phys.
102, 1143–1158 (2018).
148. Lambin, P. etal. Radiomics: the bridge between
medical imaging and personalized medicine. Nat. Rev.
Clin. Oncol. 14, 749–762 (2017).
149. Park, J. E. etal. Quality of science and reporting of
radiomics in oncologic studies: room for improvement
according to radiomics quality score and TRIPOD
statement. Eur. Radiol. 30, 523–536 (2020).
150. Khorrami, M. etal. Stable and discriminating radiomic
predictor of recurrence in early stage non- small cell
lung cancer: multi- site study. Lung Cancer 142,
90–97 (2020).
151. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch
effects in microarray expression data using empirical
Bayes methods. Biostatistics 8, 118–127 (2007).
152. Orlhac, F., Frouin, F., Nioche, C., Ayache, N. & Buvat, I.
Validation of a method to compensate multicenter
effects affecting CT radiomics. Radiology 291, 53–59
153. Kumar, V. etal. Radiomics: the process and the
challenges. Magn. Reson. Imaging 30, 1234–1248
154. Zwanenburg, A. etal. The image biomarker
standardization initiative: standardized quantitative
radiomics for high- throughput image- based
phenotyping. Radiology 295, 328–338 (2020).
155. Rudin, C. Stop explaining black box machine
learningmodels for high stakes decisions and use
interpretable models instead. Nat. Mach. Intell. 1,
206 (2019).
156. London, A. J. Artificial intelligence and black- box
medical decisions: accuracy versus explainability.
Hastings Cent. Rep. 49, 15–21 (2019).
157. Holzinger, A., Langs, G., Denk, H., Zatloukal, K.
& Müller, H. Causability and explainability of artificial
intelligence in medicine. Wiley Interdiscip. Rev. Data
Min. Knowl. Discov. 9, e1312 (2019).
158. US Food and Drug Administration. MammaPrint
510(k) premarket notification. FDA https://
cfpmn/pmn.cfm?ID=k070675 (2007).
159. US Food and Drug Administration. FDA releases
artificial intelligence/machine learning action
plan. FDA events/
press-announcements/fda- releases-artificial-
intelligencemachine-learning- action-plan (2021).
160. Institute of Medicine. Policy Issues in the Development
of Personalized Medicine in Oncology: Workshop
Summary (National Academies, 2010).
161. US Food and Drug Administration. Discussion paper
on laboratory developed tests (LDTs) (FDA, 2017).
162. Nakasu, S., Onishi, T., Kitahara, S., Oowaki, H.
& Matsumura, K. CT Hounsfield unit is a good
predictor of growth in meningiomas. Neurol. Med. Chir.
59, 54–62 (2019).
163. Urata, M. etal. Computed tomography Hounsfield
units can predict breast cancer metastasis to axillary
lymph nodes. BMC Cancer 14, 54 (2014).
164. Galloway, M. M. Texture analysis using gray level run
lengths. Comput. Graph. Image Process. 4, 172–179
165. Wang, L. & He, D.-C. Texture classification using texture
spectrum. Pattern Recognit. 23, 905–910 (1990).
166. Fogel, I. & Sagi, D. Gabor filters as texture
discriminator. Biol. Cybern. 61, 103–113 (1989).
167. Chen, S. S., Keller, J. M. & Crownover, R. M. On
thecalculation of fractal features from images. IEEE
Trans. Pattern Anal. Mach. Intell. 15, 1087–1090
168. Kontos, D. etal. Radiomic phenotypes of
mammographic parenchymal complexity: toward
augmenting breast density in breast cancer risk
assessment. Radiology 290, 41–49 (2019).
169. Yang, J. etal. Integrating tumor and nodal radiomics
to predict lymph node metastasis in gastric cancer.
Radiother. Oncol. 150, 89–96 (2020).
170. Bullitt, E. etal. Abnormal vessel tortuosity as a
marker of treatment response of malignant gliomas:
preliminary report. Technol. Cancer Res. Treat. 3,
577–584 (2004).
171. Alilou, M. etal. Quantitative vessel tortuosity:
a potential CT imaging biomarker for distinguishing
lung granulomas from adenocarcinomas. Sci. Rep. 8,
1–16 (2018).
172. Wu, C., Pineda, F., Hormuth, D. A., Karczmar, G. S.
& Yankeelov, T. E. Quantitative analysis of vascular
properties derived from ultrafast DCE- MRI to
discriminate malignant and benign breast
tumors.Magn. Reson. Med. 81, 2147–2160
Research reported in this publication was supported by the
Clinical and Translational Science Collaborative of Cleveland
(UL1TR0002548) from the National Center for Advancing
Translational Sciences (NCATS) component of the NIH and
NIHroadmap for Medical Research; the Kidney Precision
Medicine Project (KPMP) Glue Grant; the National
Cancer Institute (award numbers 1F31CA221383-01A1,
1U24CA199374-01, R01CA249992-01A1, R01CA202752-
01A1, R01CA208236-01A1, R01CA216579-01A1,
R01CA220581-01A1, R01CA257612-01A1, 1U01CA239055-
01, 1U01CA248226-01 and 1U54CA254566-01); the
National Center for Research Resources (award number 1
C06 RR12463-01); the National Heart, Lung and Blood
Institute (1R01HL15127701A1 and R01HL15807101A1);
the National Institute of Biomedical Imaging and
Bioengineering (1R43EB028736-01 and T32EB007509);
the Office of the Assistant Secretary of Defense for Health
Affairs, through the Breast Cancer Research Program
(W81XWH-19-1-0668), the Lung Cancer Research
Program (W81XWH-18-1-0440, W81XWH-20-1-0595), the
Peer Reviewed Cancer Research Program (W81XWH-
18-1-0404, W81XWH-21-1-0345) and the Prostate Cancer
Research Program (W81XWH-15-1-0558, W81XWH-20-1-
0851); the Ohio Third Frontier Technology Validation Fund;
the VA Merit Review Award IBX004121A from the United
States Department of Veterans Affairs Biomedical
Laboratory Research and Development Service; and The
Wallace H. Coulter Foundation Program in the Department
of Biomedical Engineering at Case Western Reserve
University; and through sponsored research agreements
from AstraZeneca, Boehringer Ingelheim and Bristol Myers
Squibb. The content is solely the responsibility of the authors
and does not necessarily represent the official views of any
of the institutions named.
Author contributions
K.B., N.B. and A.M. researched data for this manuscript. All
authors contributed to all other aspects of preparation of this
Competing interests
N.B. is a current employee of Tempus Labs and a former
employee of IBM Research, with both of which he is an inven-
tor on several pending patents pertaining to medical image
analysis. He additionally holds equity in Tempus Labs. V.V. is
a consultant for Alkermes, AstraZeneca, Bristol Myers Squibb,
Celgene, Foundation Medicine, Genentech, Merck, Nektar
Therapeutics and Takeda, has current or pending grants from
Alkermes, AstraZeneca, Bristol Myers Squibb, Genentech and
Merck, is on the speakers’ bureaus of Bristol Myers Squibb,
Celgene, Foundation Medicine and Novartis, and has received
payment for the development of educational presentations
from Bristol Myers Squibb and Foundation Medicine. A.M.
holds equity in Elucid Bioimaging and Inspirata, has been or
is a scientific advisory board member for Aiforia, AstraZeneca,
Bristol Myers Squibb, Inspirata and Merck, serves as a con-
sultant for Caris, Inc. and Roche Diagnostics, has sponsored
research agreements with AstraZeneca, Boehringer
Ingelheim, Bristol Myers Squibb and Philips, has developed a
technology relating to cardiovascular imaging that has been
licensed to Elucid Bioimaging, and is involved in an NIH U24
grant with PathCore and three different NIH R01 grants with
Inspirata. The other authors declare no competing interests.
Peer review information
Nature Reviews Clinical Oncology thanks D. Kontos, R. Mak
and the other, anonymous, reviewer(s) for their contribution to
the peer review of this work.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional
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Supplementary information
The online version contains supplementary material available
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© Springer Nature Limited 2021
Nature reviews
CliniCal OnCOlOgy
... Recently, the idea that imaging studies contain a great quantity of data, in form of grey level patterns, which are imperceptible to the human eyes, has become more and more interesting [68][69][70][71][72][73][74][75][76][77][78][79][80]. These texture features, when correlated with clinical-pathological data and outcomes [81][82][83][84][85][86][87][88][89][90][91][92][93], theoretically allow diagnostic and prognostic assessment . ...
Full-text available
In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.
... Tumor heterogeneity in pre-treatment 18 F-FDG PET/CT images may contribute to better characterization and improved prediction of malignant tumor survival outcomes [5]. Recently, radiomics has become the primary trend in medical imaging studies and has shown significant potential for the analysis of lesion characterization and intratumoral heterogeneity (ITH) [6,7]. It can extract large numbers of quantitative multidimensional and subvisual image features and excavate a large amount of image information to evaluate the biological characteristics of tumors [8][9][10]. ...
Full-text available
Background and purpose: To investigate the value of radiomics features extracted from pre-treatment 18F-FDG PET/CT in predicting the outcomes of stage III-IV colorectal cancer (CRC), which may assist in clinical management strategies and precise treatment of stage III-IV CRC. Materials and methods: 124 patients with pathologically confirmed stage III-IV CRC who underwent pre-treatment 18F-FDG PET/CT scans were enrolled in this study. The least absolute shrinkage and selection operator Cox regression (LASSO-Cox) was used to select radiomics features, and the radiomics scores (Rad-scores) were calculated to build radiomics models. The performance of radiomics models was represented by the concordance index (C-index) and compared with clinical models and complex model. The bootstrap resampling method was used to create validation sets. Additionally, nomograms were developed based on complex models. Results: The C-indices of the radiomics model for predicting PFS and OS were 0.712 (95%CI: 0.680-0.744) and 0.758 (0.728-0.789), respectively. In the clinical model, these values were 0.690 (0.664-0.0.717) and 0.738 (0.709-0.767), respectively. However, in the complex model were 0.734 (0.705-0.762) and 0.780 (0.754-0.807), respectively. The Kaplan-Meier curves demonstrated that the radiomics model could effectively separate patients with stage III-IV stage CRC into high- and low-risk groups (p < 0.001). Multivariate Cox regression analysis confirmed the independent prognostic value of Rad-scores. Conclusion: Pre-treatment 18F-FDG PET/CT radiomics features can stratify the risk of patients with stage III-IV CRC and accurately predict their outcomes. These findings could be clinically valuable for precision treatment and management decisions in stage III-IV CRC.
Conference Paper
Full-text available
در سا¬ل¬های اخیر، فناوری هوش مصنوعی به سرعت توسعه یافته است. به ویژه، در بینایی کامپیوتر، پردازش تصویر و تجزیه و تحلیل داده‌ها، هوش مصنوعی دستاوردهای مهمی را به همراه داشته است. کاربردهای هوش مصنوعی در حوزه تشخیص بیماریها و سرطان بسیار حائز اهمیت است زیرا با استفاده از الگوریتم¬های هوش مصنوعی می‌توان شناسایی بیماری‌ها را با دقت زیاد، تعداد بالا و به صورت 24ساعته انجام داد. همچنین الگوریتم¬های مبتنی بر هوش مصنوعی، به ویژه آنهایی که مبتنی بر یادگیری عمیق هستند، دارای برتری هایی هستند که سبب می‌شود تا توسعه این سیستم های تشخیص بیماری بهتر و بیشتر توسعه داده شود. با پیشرفت صنعت تصویربرداری و ظهور فناوری¬های جدیدی مانند مانند مایکروویو و ترموگرافی، پردازش تصویر نیز از الگوریتم¬های جدیدی برای تشخیص بیماری استفاده می‌کند. فناوری تشخیص هوش مصنوعی قادر به انجام تجزیه و تحلیل خوشه چند پارامتری و ساده کردن تصویر می¬باشد، بنابراین به پزشکان در غربالگری اولیه سرطان‌ها کمک بسیاری می¬کند. به طور کلی، هوش مصنوعی رشد قابل توجهی در سلامت پزشکی نشان داده است و در تصویربرداری های مختلف و تجزیه و تحلیل بیماری‌ها نقش بسیار موثری دارد. تشخیص سرطان از روی تصویر نیز مشکلات زیادی دارد که می توان از الگوریتم¬های هوش مصنوعی استفاده کرد و با ایجاد یک مدل هوشمند برای تجزیه و تحلیل تصاویر سرطان استفاده کرد.
Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.
Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.
Objectives: To assess whether integrative radiomics and transcriptomics analyses could provide novel insights for radiomic features' molecular annotation and effective risk stratification in non-small cell lung cancer (NSCLC). Methods: A total of 627 NSCLC patients from three datasets were included. Radiomics features were extracted from segmented 3-dimensional tumour volumes and were z-score normalized for further analysis. In transcriptomics level, 186 pathways and 28 types of immune cells were assessed by using the Gene Set Variation Analysis (GSVA) algorithm. NSCLC patients were categorized into subgroups based on their radiomic features and pathways enrichment scores using consensus clustering. Subgroup-specific radiomics features were used to validate clustering performance and prognostic value. Kaplan-Meier survival analysis with the log-rank test and univariable and multivariable Cox analyses were conducted to explore survival differences among the subgroups. Results: Three radiotranscriptomics subtypes (RTSs) were identified based on the radiomics and pathways enrichment profiles. The three RTSs were characterized as having specific molecular hallmarks: RTS1 (proliferation subtype), RTS2 (metabolism subtype), and RTS3 (immune activation subtype). RTS3 showed increased infiltration of most immune cells. The RTS stratification strategy was validated in a validation cohort and showed significant prognostic value. Survival analysis demonstrated that the RTS strategy could stratify NSCLC patients according to prognosis (p = 0.009), and the RTS strategy remained an independent prognostic indicator after adjusting for other clinical parameters. Conclusions: This radiotranscriptomics study provides a stratification strategy for NSCLC that could provide information for radiomics feature molecular annotation and prognostic prediction. Key points: • Radiotranscriptomics subtypes (RTSs) could be used to stratify molecularly heterogeneous patients. • RTSs showed relationships between molecular phenotypes and radiomics features. • The RTS algorithm could be used to identify patients with poor prognosis.
Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically require single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in the clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in oncology image-recognition tasks assisting physicians in accurate diagnosis. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics which provides critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based upon routine clinical radiological scans and whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesize the general framework of molecular intelligent diagnostics beyond standard techniques. Then we summarize the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. However, despite that, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.
Lung cancer is the leading cause of cancer-related deaths worldwide. Medical imaging technologies such as computed tomography (CT) and positron emission tomography (PET) are routinely used for non-invasive lung cancer diagnosis. In clinical practice, physicians investigate the characteristics of tumors such as the size, shape and location from CT and PET images to make decisions. Recently, scientists have proposed various computational image features that can capture more information than that directly perceivable by human eyes, which promotes the rise of radiomics. Radiomics is a research field on the conversion of medical images into high-dimensional features with data-driven methods to help subsequent data mining for better clinical decision support. Radiomic analysis has four major steps: image preprocessing, tumor segmentation, feature extraction and clinical prediction. Machine learning, including the high-profile deep learning, facilitates the development and application of radiomic methods. Various radiomic methods have been proposed recently, such as the construction of radiomic signatures, tumor habitat analysis, cluster pattern characterization and end-to-end prediction of tumor properties. These methods have been applied in many studies aiming at lung cancer diagnosis, treatment and monitoring, shedding light on future non-invasive evaluations of the nodule malignancy, histological subtypes, genomic properties and treatment responses. In this review, we summarized and categorized the studies on the general workflow, methods for clinical prediction and clinical applications of machine learning in lung cancer radiomic studies, introduced some commonly-used software tools, and discussed the limitations of current methods and possible future directions.
The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.
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As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past decade. On the other hand, recent advances in deep learning and transfer learning have shown significant potential in the quantitative medical imaging field, raising the research question of whether deep transfer learning features have predictive information in addition to radiomics features. In this study, using CT images from Pancreatic Ductal Adenocarcinoma (PDAC) patients recruited in two independent hospitals, we discovered most transfer learning features have weak linear relationships with radiomics features, suggesting a potential complementary relationship between these two feature sets. We also tested the prognostic performance for overall survival using four feature fusion and reduction methods for combining radiomics and transfer learning features and compared the results with our proposed risk score-based feature fusion method. It was shown that the risk score-based feature fusion method significantly improves the prognosis performance for predicting overall survival in PDAC patients compared to other traditional feature reduction methods used in previous radiomics studies (40% increase in area under ROC curve (AUC) yielding AUC of 0.84).
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Importance Axillary lymph node metastasis (ALNM) status, typically estimated using an invasive procedure with a high false-negative rate, strongly affects the prognosis of recurrence in breast cancer. However, preoperative noninvasive tools to accurately predict ALNM status and disease-free survival (DFS) are lacking. Objective To develop and validate dynamic contrast–enhanced magnetic resonance imaging (DCE-MRI) radiomic signatures for preoperative identification of ALNM and to assess individual DFS in patients with early-stage breast cancer. Design, Setting, and Participants This retrospective prognostic study included patients with histologically confirmed early-stage breast cancer diagnosed at 4 hospitals in China from July 3, 2007, to September 21, 2019, randomly divided (7:3) into development and vaidation cohorts. All patients underwent preoperative MRI scans, were treated with surgery and sentinel lymph node biopsy or ALN dissection, and were pathologically examined to determine the ALNM status. Data analysis was conducted from February 15, 2019, to March 20, 2020. Exposure Clinical and DCE-MRI radiomic signatures. Main Outcomes and Measures The primary end points were ALNM and DFS. Results This study included 1214 women (median [IQR] age, 47 [42-55] years), split into development (849 [69.9%]) and validation (365 [30.1%]) cohorts. The radiomic signature identified ALNM in the development and validation cohorts with areas under the curve (AUCs) of 0.88 and 0.85, respectively, and the clinical-radiomic nomogram accurately predicted ALNM in the development and validation cohorts (AUC, 0.92 and 0.90, respectively) based on a least absolute shrinkage and selection operator (LASSO)–logistic regression model. The radiomic signature predicted 3-year DFS in the development and validation cohorts (AUC, 0.81 and 0.73, respectively), and the clinical-radiomic nomogram could discriminate high-risk from low-risk patients in the development cohort (hazard ratio [HR], 0.04; 95% CI, 0.01-0.11; P < .001) and the validation cohort (HR, 0.04; 95% CI, 0.004-0.32; P < .001) based on a random forest–Cox regression model. The clinical-radiomic nomogram was associated with 3-year DFS in the development and validation cohorts (AUC, 0.89 and 0.90, respectively). The decision curve analysis demonstrated that the clinical-radiomic nomogram displayed better clinical predictive usefulness than the clinical or radiomic signature alone. Conclusions and Relevance This study described the application of MRI-based machine learning in patients with breast cancer, presenting novel individualized clinical decision nomograms that could be used to predict ALNM status and DFS. The clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.
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Background We evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC. Methods This study enrolled of 739 consecutive stage II/III GC patients. Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation. Results The radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P <0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy. Conclusion The newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy.
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Purpose Hyperprogression is an atypical response pattern to immune checkpoint inhibition that has been described within non-small cell lung cancer (NSCLC). The paradoxical acceleration of tumor growth after immunotherapy has been associated with significantly shortened survival, and currently, there are no clinically validated biomarkers to identify patients at risk of hyperprogression. Experimental design A total of 109 patients with advanced NSCLC who underwent monotherapy with Programmed cell death protein-1 (PD1)/Programmed death-ligand-1 (PD-L1) inhibitors were included in the study. Using RECIST measurements, we divided the patients into responders (n=50) (complete/partial response or stable disease) and non-responders (n=59) (progressive disease). Tumor growth kinetics were used to further identify hyperprogressors (HPs, n=19) among non-responders. Patients were randomized into a training set (D 1 =30) and a test set (D 2 =79) with the essential caveat that HPs were evenly distributed among the two sets. A total of 198 radiomic textural patterns from within and around the target nodules and features relating to tortuosity of the nodule associated vasculature were extracted from the pretreatment CT scans. Results The random forest classifier using the top features associated with hyperprogression was able to distinguish between HP and other radiographical response patterns with an area under receiver operating curve of 0.85±0.06 in the training set (D 1 =30) and 0.96 in the validation set (D 2 =79). These features included one peritumoral texture feature from 5 to 10 mm outside the tumor and two nodule vessel-related tortuosity features. Kaplan-Meier survival curves showed a clear stratification between classifier predicted HPs versus non-HPs for overall survival (D 2 : HR=2.66, 95% CI 1.27 to 5.55; p=0.009). Conclusions Our study suggests that image-based radiomics markers extracted from baseline CTs of advanced NSCLC treated with PD-1/PD-L1 inhibitors may help identify patients at risk of hyperprogressions.
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Rationale and Objectives Peritumoral features have been suggested to be useful in improving the prediction performance of radiomic models. The aim of this study is to systematically investigate the prediction performance improvement for sentinel lymph node (SLN) status in breast cancer from peritumoral features in radiomic analysis by exploring the effect of peritumoral region sizes. Materials and Methods This retrospective study was performed using dynamic contrast-enhanced MRI scans of 162 breast cancer patients. The effect of peritumoral features was evaluated in a radiomics pipeline for predicting SLN metastasis in breast cancer. Peritumoral regions were generated by dilating the tumor regions-of-interest (ROIs) manually annotated by two expert radiologists, with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm. The prediction models were established in the training set (∼67% of cases) using the radiomics pipeline with and without peritumoral features derived from different peritumoral thicknesses. The prediction performance was tested in an independent validation set (the remaining ∼33%). Results For this specific application, the accuracy in the validation set when using the two radiologists’ ROIs could be both improved from 0.704 to 0.796 by incorporating peritumoral features. The choice of the peritumoral size could affect the level of improvement. Conclusion This study systematically investigates the effect of peritumoral region sizes in radiomic analysis for prediction performance improvement. The choice of the peritumoral size is dependent on the ROI drawing and would affect the final prediction performance of radiomic models, suggesting that peritumoral features should be optimized in future radiomics studies.
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Two major treatment strategies employed in non-small cell lung cancer, NSCLC, are tyrosine kinase inhibitors, TKIs, and immune checkpoint inhibitors, ICIs. The choice of strategy is based on heterogeneous biomarkers that can dynamically change during therapy. Thus, there is a compelling need to identify comprehensive biomarkers that can be used longitudinally to help guide therapy choice. Herein, we report a 18F-FDG-PET/CT-based deep learning model, which demonstrates high accuracy in EGFR mutation status prediction across patient cohorts from different institutions. A deep learning score (EGFR-DLS) was significantly and positively associated with longer progression free survival (PFS) in patients treated with EGFR-TKIs, while EGFR-DLS is significantly and negatively associated with higher durable clinical benefit, reduced hyperprogression, and longer PFS among patients treated with ICIs. Thus, the EGFR-DLS provides a non-invasive method for precise quantification of EGFR mutation status in NSCLC patients, which is promising to identify NSCLC patients sensitive to EGFR-TKI or ICI-treatments. EGFR mutations are common in non-small cell lung cancer and patients with these mutations are treated with tyrosine kinase inhibitors. Here, the authors show that EGFR mutation status can be predicted from 18F-FDG-PET/CT images, which may enable the stratification of patients for treatment.
Rationale and Objectives Standardized Uptake Value (SUV) is an important semiquantitative measurement used in the clinical and research domains to assess radiopharmaceutical concentration in tumors versus normal organs, but is susceptible to many factors beyond the tumor biological environment. So, the aim of this study is to identify the optimum internal reference among organs with physiological uptake in ⁶⁸Ga-DOTATATE PET/CT (DOTA PET/CT) scans. Materials and Methods This HIPAA-compliant, IRB-approved study with waiver of consent included retrospective imaging review of 180 consecutive patients with neuroendocrine tumors presenting for DOTA PET/CT image acquisition: Ga-⁶⁸ DOTATATE dose was reported as (0.054 mCi/Kg) scans between September 2018 and May 2019. Mean value of body weight normalized SUV (SUVbw) and lean body mass normalized SUV (SUL) of liver and spleen were measured. Information about the patients and scan characteristics were collected. The paired Grambsch test was used to compare variance among the measured SUVs. Spearman's rank correlation coefficient was used to assess correlation between SUVs and potential patient- and scan-specific confounding factors. Results Variance of SUL was significantly lower than variance of SUVbw in both liver and spleen (p-value < 0.0001). Variances of liver SUVbw and SUL were significantly lower than the corresponding spleen SUVs. Liver SUL showed the lowest variance (3.69% ± 1.25%) among all measured SUVs. Conclusion SUL is a more reproducible, less variable, and therefore more reliable quantitative measure in DOTA PET/CT scans, compared SUVbw. Among the available organs with physiological uptake, liver SUL is the optimum internal reference given the liver's larger size and uniform SUL values resulting in lower variability and better reproducibility.
Purpose: To explore the potential of radiomics texture features as potential biomarkers to enable detection of the presence of BRAF mutation and prediction of 5-year overall survival (OS) in stage IV colorectal cancer (CRC). Materials and methods: In this retrospective study, a total of 145 patients (mean age, 61 years ± 14 [standard deviation {SD}]; 68 female patients and 77 male patients) with stage IV CRC who underwent molecular profiling and pretreatment contrast material-enhanced CT scans between 2004 and 2018 were included. Tumor radiomics texture features, including the mean, the SD, the mean value of positive pixels (MPP), skewness, kurtosis, and entropy, were extracted from regions of interest on CT images after applying three Laplacian-of-Gaussian filters known as spatial scaling factors (SSFs) (SSF = 2, fine; SSF = 4, medium; SSF = 6, coarse) by using specialized software; values of these parameters were also obtained without filtration (SSF = 0). The Wilcoxon rank sum test was used to assess differences between mutated versus wild-type BRAF tumors. Associations between radiomics texture features and 5-year OS were determined by using Kaplan-Meier estimators using the log-rank test and multivariate Cox proportional-hazards regression analysis. Results: The SDs and MPPs of radiomic texture features were significantly lower in BRAF mutant tumors than in wild-type BRAF tumors at SSFs of 0, 4, and 6 (P = .006, P = .007, and P = .005, respectively). Patients with skewness less than or equal to -0.75 at an SSF of 0 and a mean of greater than or equal to 17.76 at an SSF of 2 showed better 5-year OS (hazard ratio [HR], 0.53 [95% confidence interval {CI}: 0.29, 0.94]; HR, 0.40 [95% CI: 0.22, 0.71]; log-rank P = .025 and P = .002, respectively). Tumor location (right colon vs left colon vs rectum) had no significant impact on the clinical outcome (log-rank P = .53). Conclusion: Radiomics texture features can serve as potential biomarkers for determining BRAF mutation status and as predictors of 5-year OS in patients with advanced-stage CRC.Keywords: Abdomen/GI, CT, Comparative Studies, Large BowelSupplemental material is available for this article.© RSNA, 2020.
Background: Currently, there are no methods to identify patients with an increased risk of liver metastases to guide patient selection for liver-directed therapies. We tried to determine whether quantitative image features (radiomics) of the liver obtained from preoperative staging CT scans at the time of initial colon resection differ in patients that subsequently develop liver metastases, extrahepatic metastases, or demonstrate prolonged disease-free survival. Methods: Patients who underwent resection of stage II/III colon cancer from 2004 to 2012 with available preoperative CT scans were included in this single-institution, retrospective case-control study. Patients were grouped by initial recurrence patterns: liver recurrence, extrahepatic recurrence, or no evidence of disease at 5 years. Radiomic features of the liver parenchyma extracted from CT images were compared across groups. Results: The cohort consisted of 120 patients divided evenly between three recurrence groups, with an equal number of stage II and III patients in each group. After adjusting for multiple comparisons, 44 of 254 (17%) imaging features displayed different distributions across the three patient groups (p < 0.05), with the clearest distinction between those with liver recurrence and no evidence of disease. Increased heterogeneity in the liver parenchyma by radiomic analysis was protective of liver metastases. Conclusions: CT radiomics is a promising tool to identify patients at high risk of developing liver metastases and is worthy of further investigation and validation.