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Tumor Infiltration in Enhancing and Non-Enhancing Parts of Glioblastoma: A Correlation with Histopathology

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Purpose To correlate histopathologic findings from biopsy specimens with their corresponding location within enhancing areas, non-enhancing areas and necrotic areas on contrast enhanced T1-weighted MRI scans (cT1). Materials and Methods In 37 patients with newly diagnosed glioblastoma who underwent stereotactic biopsy, we obtained a correlation of 561 1mm³ biopsy specimens with their corresponding position on the intraoperative cT1 image at 1.5 Tesla. Biopsy points were categorized as enhancing (CE), non-enhancing (NE) or necrotic (NEC) on cT1 and tissue samples were categorized as “viable tumor cells”, “blood” or “necrotic tissue (with or without cellular component)”. Cell counting was done semi-automatically. Results NE had the highest content of tissue categorized as viable tumor cells (89% vs. 60% in CE and 30% NEC, respectively). Besides, the average cell density for NE (3764 ± 2893 cells/mm²) was comparable to CE (3506 ± 3116 cells/mm²), while NEC had a lower cell density with 2713 ± 3239 cells/mm². If necrotic parts and bleeds were excluded, cell density in biopsies categorized as “viable tumor tissue” decreased from the center of the tumor (NEC, 5804 ± 3480 cells/mm²) to CE (4495 ± 3209 cells/mm²) and NE (4130 ± 2817 cells/mm²). Discussion The appearance of a glioblastoma on a cT1 image (circular enhancement, central necrosis, peritumoral edema) does not correspond to its diffuse histopathological composition. Cell density is elevated in both CE and NE parts. Hence, our study suggests that NE contains considerable amounts of infiltrative tumor with a high cellularity which might be considered in resection planning.
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RESEARCH ARTICLE
Tumor Infiltration in Enhancing and Non-
Enhancing Parts of Glioblastoma: A
Correlation with Histopathology
Oliver Eidel
1,2,3
, Sina Burth
1,2,3
, Jan-Oliver Neumann
4
, Pascal J. Kieslich
5
, Felix Sahm
6
,
Christine Jungk
4
, Philipp Kickingereder
1
, Sebastian Bickelhaupt
2
,
Sibu Mundiyanapurath
7
, Philipp Ba
¨umer
2
, Wolfgang Wick
7
, Heinz-Peter Schlemmer
2
,
Karl Kiening
4
, Andreas Unterberg
4
, Martin Bendszus
1
, Alexander Radbruch
1,2,3
*
1Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany,
2Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 3German
Cancer Consortium (DKTK), Radiology, Heidelberg, Germany, 4Department of Neurosurgery, Division
Stereotactic Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany, 5Department of
Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany, 6Department of
Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany, 7Department of Neurology,
University of Heidelberg Medical Center, Heidelberg, Germany
*a.radbruch@dkfz.de
Abstract
Purpose
To correlate histopathologic findings from biopsy specimens with their corresponding loca-
tion within enhancing areas, non-enhancing areas and necrotic areas on contrast enhanced
T1-weighted MRI scans (cT1).
Materials and Methods
In 37 patients with newly diagnosed glioblastoma who underwent stereotactic biopsy, we
obtained a correlation of 561 1mm
3
biopsy specimens with their corresponding position on
the intraoperative cT1 image at 1.5 Tesla. Biopsy points were categorized as enhancing
(CE), non-enhancing (NE) or necrotic (NEC) on cT1 and tissue samples were categorized
as “viable tumor cells”, “blood” or “necrotic tissue (with or without cellular component)”. Cell
counting was done semi-automatically.
Results
NE had the highest content of tissue categorized as viable tumor cells (89% vs. 60% in CE
and 30% NEC, respectively). Besides, the average cell density for NE (3764 ±2893 cells/
mm
2
) was comparable to CE (3506 ±3116 cells/mm
2
), while NEC had a lower cell density
with 2713 ±3239 cells/mm
2
. If necrotic parts and bleeds were excluded, cell density in biop-
sies categorized as “viable tumor tissue” decreased from the center of the tumor (NEC,
5804 ±3480 cells/mm
2
) to CE (4495 ±3209 cells/mm
2
) and NE (4130 ±2817 cells/mm
2
).
PLOS ONE | DOI:10.1371/journal.pone.0169292 January 19, 2017 1 / 12
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OPEN ACCESS
Citation: Eidel O, Burth S, Neumann J-O, Kieslich
PJ, Sahm F, Jungk C, et al. (2017) Tumor
Infiltration in Enhancing and Non-Enhancing Parts
of Glioblastoma: A Correlation with Histopathology.
PLoS ONE 12(1): e0169292. doi:10.1371/journal.
pone.0169292
Editor: Christoph Kleinschnitz, Julius-Maximilians-
Universita¨t Wu¨rzburg, GERMANY
Received: June 23, 2016
Accepted: December 14, 2016
Published: January 19, 2017
Copyright: ©2017 Eidel et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Since the used data
are patient data they are available from the
corresponding author Alexander Radbruch
(a.radbruch@dkfz.de) for researchers who meet
the criteria for access to confidential data.
Funding: S. Burth is supported by the Mildred-
Scheel-Doktorandenprogramm of the German
Cancer Aid (grant: 111583). The funder had no role
in study design, data collection and analysis,
decision to publish, or preparation of the
manuscript.
Discussion
The appearance of a glioblastoma on a cT1 image (circular enhancement, central necrosis,
peritumoral edema) does not correspond to its diffuse histopathological composition. Cell
density is elevated in both CE and NE parts. Hence, our study suggests that NE contains
considerable amounts of infiltrative tumor with a high cellularity which might be considered
in resection planning.
Introduction
MRI is the most important non-invasive diagnostic tool for the assessment of glioblastoma[1],
the most common type of malignant brain tumor in adults [2]. T1- and T2-weighted sequences
are complemented by functional MRI measurements such as diffusion- and perfusion-
weighted MRI which provide additional pathophysiological information. Generally, all MRI
sequences reflect physical properties of the tissue and are not necessarily tumor specific. In
particular, contrast-enhancing parts on T1-weighted images reflect a disruption of the blood
brain barrier that enables contrast agents to leak in the surrounding tissue and do not neces-
sarily represent solely viable tumor tissue. [3]
To be aware of the degree of concurrence of this MRI-based classification with the actual
microscopic appearance of a glioblastoma is of importance in research and clinical practice. In
the former, this especially holds true for region of interest (ROI)-based analysis of different
functional MRI parameters where the cT1 image is often used as a draft for the ROI.
In the latter, this might be of interest for the extent of surgical resection of glioblastoma,
which has shown to be a prognostic marker for patient outcome[48]. Currently, the resection
margins of the tumor are determined by conventional microsurgery with white light, by fluo-
rescence guided resection with 5-aminolevulinic acid or intra-operative contrast enhanced
MRI[9]. The absence of residual contrast enhancement on cT1 is usually interpreted as macro-
scopic complete resection. But in regard to the fast recurrence of glioblastomas after surgery
even with complete resection[8], it is of interest to know to what extent the zone of T1-contrast
enhancement does in fact correspond to the area of tumor infiltration and whether it is suit-
able to demarcate the margins of the main focus of the glioblastoma.
In this study, we correlated 561 tumor biopsies of 37 patients with newly diagnosed glio-
blastoma with the corresponding areas on the intraoperative contrast enhanced T1-weighted
MRI to determine the accuracy of the contrast enhanced T1 MRI to distinguish necrotic foci
from areas of infiltrative tumor with high cellularity.
Materials and Methods
Patients
This retrospective study was approved by the local ethics committee of the University of Hei-
delberg. Due to the retrospective nature of the study and the reduced life expectancy of the
glioblastoma patients, informed consent was waived by the ethical committee. All patients had
consented to the scientific use of their data with admission to our hospital. The database of our
department of neuropathology was screened for patients who had undergone stereotactic
biopsy between January 2010 and December 2013 and were thereafter diagnosed with glioblas-
toma WHO grade IV. Thirty-seven patients (18 male, 19 female, median age 63 ±13 years)
Glioblastoma Infiltration: Correlation of Histopathology and MRI
PLOS ONE | DOI:10.1371/journal.pone.0169292 January 19, 2017 2 / 12
Competing Interests: The authors have declared
that no competing interests exist.
with an intraoperative MRI scan during biopsy surgery were selected. Other analyses based on
the same patient cohort are reported in Eidel et al [10]
MRI imaging
Each patient received an intraoperative MRI scan for trajectory planning which was performed
in a 1.5 Tesla MRI scanner (Syngo MR B15, Siemens AG Healthcare, Erlangen) with the fol-
lowing parameters: TR = 9ms, TE = 2.38ms, flip angle 10˚, FoV 260x260mm, voxel size
(1.035mm)
3
, image matrix 256x256.
Biopsies and trajectory planning
The department of neurosurgery performed the biopsy 1–21 days (median 5 days) after the
first suspected diagnosis of a glioblastoma (which was based on MR imaging) and before any
therapy had been administered. Under general anesthesia, a stereotactic biopsy ring was
adjusted to the patient’s skull. Via intraoperative MRI, the attending neurosurgeon calculated
a trajectory (iPS software, inomed Medizintechnik GmbH, Emmendingen, Germany) from an
entry point at the skull to a target point in the contrast enhancing zone of the cT1 image. A
total of 9–22 (median: 15) biopsies were taken along the trajectory and labeled to reconstruct
their exact point of origin. The entry point and the target point of the biopsy trajectory were
transferred to the intraoperative cT1 image using a custom in-house MATLAB script
(MATLAB 2014b, The Mathworks, Natick, MA, USA). They formed the origin and the termi-
nal point within a Cartesian coordinate system from which the unit vector of the trajectory
could be derived via vector analysis. Thus, the exact coordinates of all biopsy points along the
trajectory could be calculated as the distance between each biopsy point that was given in the
pathology report (Fig 1).
We obtained a total of 561 biopsy samples, approximately 1mm
3
in size that were sent to
the department of neuropathology for further analysis and diagnosis. Each specimen was cut
into 4–8 slices and stained with hematoxylin and eosin (HE stain). All biopsies were graded as
glioblastoma (WHO grade IV) by a neuropathologist. For further post-processing, they were
scanned at x20 magnification and saved as NDPI files.
Postprocessing of the biopsies
All biopsies were categorized into four groups by consensus of a neuropathologist (FS) and a
neuroradiologist (AR) according to their histologic appearance: predominantly blood cells,
pure necrosis without cells, necrosis with a cellular component (>50% necrosis) or viable
tumor tissue; viable tumor tissue is defined as mostly tumor tissue (50% necrosis) with
tumor cells. Moreover, they were assigned to 3 groups according to their position on the
cT1 MRI: necrosis (NEC), contrast-enhancement (CE), or peritumoral non-enhancing (NE)
(Fig 2).
Biopsy specimen histologically classified as viable tumor tissue or necrosis with cellular
component were further post-processed for cell density calculation using NIH ImageJ, 64-bit
version[11]. First, images were opened using NDPI Tools[12] or split into smaller-sized images
if opening failed and subsequently converted to 8-bit (grey-scale). Cell density was calculated
semi-automatically with the ImageJ plugin ITCN in multiple steps: First, up to 8 representative
regions of cells were selected. Then, cell density calculation was performed on each region by
entering an estimate of cell width and cell spacing as input. The ITCN plugin (Fig 3). A neuro-
pathologist (FS) checked the correctness of cell detection. Cell density was then calculated for
each slice per biopsy specimen and averaged.
Glioblastoma Infiltration: Correlation of Histopathology and MRI
PLOS ONE | DOI:10.1371/journal.pone.0169292 January 19, 2017 3 / 12
Statistical analysis
Statistical analysis was performed with the R language and environment for statistical comput-
ing (version 3.2.2, R Foundation for Statistical Computing, Vienna, Austria). First, the histo-
logical composition of the three different MRI categories (CE, NE and NEC) was analyzed.
The percentage of biopsies classified as “necrosis” (with or without cellular component),
“blood” or “viable tumor tissue” in each of the three MRI categories was calculated and com-
pared using a χ
2
test. Second, the cell density in the three different MRI categories was com-
pared. Boxplots of the cell densities in the different MRI categories were created. Besides, the
mean (and standard deviation) of the cell densities in the different MRI categories was calcu-
lated. Contrast analyses in a linear model and in a linear-mixed model including a random
intercept for the individual patients were carried out to test whether the cell density was signif-
icantly different in biopsies from the three different MRI categories (one contrast comparing
NEC and CE/NE, and another comparing CE and NE).
Results
Histological composition of the different MRI classifications
Of the total 561 biopsies, 321 (57.2%) originated from CE, 103 (18.4%) from NE and 137
(24.4%) from NEC. Histologically, 327 biopsies (58.3%) were classified as viable tumor cells,
175 (31.2%) as necrosis with cellular component, 11 (2.0%) as pure necrosis and 48 (8.5%) as
blood cells (Table 1).
The relative frequency of the different histologic classifications within each MRI classifica-
tion is displayed in Fig 4. We found that areas of contrast enhancement (CE) were composed
Fig 1. Calculation of the biopsy point S that is located in the enhancing tumor area (CE). Combination
of an axial and a coronal slide of intraoperative cT1 MRI in a 60-year-old patient with glioblastoma. Cranial
view. The biopsy point S is marked within the NE-area of the tumor. The trajectory (red line, length: 50.4mm)
and the biopsy point S (170/113/37) were calculated via vector analysis in MATLAB from the coordinates of
the known entry point E (192/121/54) and the target point T (161/152/30). The distance between S and T was
15.0 mm.
doi:10.1371/journal.pone.0169292.g001
Glioblastoma Infiltration: Correlation of Histopathology and MRI
PLOS ONE | DOI:10.1371/journal.pone.0169292 January 19, 2017 4 / 12
of 60% viable tumor cells, 31% necrosis with cellular component, 1% pure necrosis and 8%
blood cells. Necrotic areas on the MRI (NEC) contained 30% viable tumor cells, 51% necrosis
with cellular component, 4% pure necrosis and 15% blood cells. The non-enhancing part on
the cT1 MRI (NE) contained 89% viable tumor cells, 5% necrosis with cellular component, 3%
pure necrosis and 3% blood cells. A χ
2
test revealed a significant relationship between the pat-
terns of histopathologic composition and the classification of the MRI-location in which the
biopsy was found (χ
2
(6) = 92.29, p <0.001). Comparing the NE zone to the CE and NEC
zone, the relative content of “viable tumor cells” was significantly higher in the NE zone
Fig 2. Correlation of cT1 MRI and histology. A) Target point T with its coordinates in an axial slide of the
intraoperative cT1 MRI of the patient from Fig 1a. It lies in the CE area. B) Corresponding slice of the 1mm
3
biopsy specimen (HE stain) in x20 magnification which was classified as “necrosis with cellular component”.
This type of histology occurred in 31% of all biopsies originating from CE. C) Calculated biopsy point C with its
coordinates in an axial slide of the intraoperative cT1 MRI of an 82-year-old patient with glioblastoma. It lies in
the CE area. D) Corresponding slice of the 1mm
3
biopsy specimen (HE stain) in x20 magnification which was
classified as “viable tumor tissue”. This type of histology occurred in 60% of all biopsies originating from CE.
E) Different biopsy point D with its coordinates along the trajectory in the same patient. It lies in the NEC area.
F) Corresponding slice of the 1mm
3
biopsy specimen (HE stain) in x20 magnification which was classified as
“pure necrosis”. This type of histology occurred in 4% of all biopsies originating from NEC.
doi:10.1371/journal.pone.0169292.g002
Glioblastoma Infiltration: Correlation of Histopathology and MRI
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(χ
2
(2) = 86.73, p <0.001). This was also confirmed in a generalized linear model using a bino-
mial link function (both with and without random intercept for the individual patients).
Cellularity
Mean cell density within all biopsies that were classified as “viable tumor cells” was
4557 ±3169 cells/mm
2
. In the category “necrosis with cellular component”, cell density was
2255 ±2204 cells/mm
2
. Boxplots of the cell densities in the MRI categories are displayed in Fig
5. The mean cell densities per MRI classification are summarized in Table 2.
Split up into the MRI classifications and assuming that the tumor cell density in “blood
cells” and “pure necrosis” was zero, the cell density in MRI-necrosis was significantly lower
than in the other two MRI classifications in the linear model (t(558) = -2.90, p = 0.004)
(Table 2 and Fig 5A). Moreover, cell densities did not differ significantly between CE and
NE (t(558) = -0.73, p = 0.46). Repeating the analyses with a linear-mixed model, both compari-
sons were not significant (t(557.8) = -0.65, p = 0.52 for NEC vs. CE/NE, and t(557.7) = 1.59,
p = 0.11 for CE vs. NE).
If we only consider cell density within the biopsies that classified as “viable tumor cells”,
excluding necrotic parts and bleeds that dilute overall cell density, cell density was highest in
NECcompared to the other two MRI classifications (t(324) = 2.82, p = 0.005) (Table 2 and
Fig 5B). Additionally, CEshowed a higher cell density than NE–however, this difference was
not significant (t(324) = 0.92, p = 0.36). In a linear-mixed model, both comparisons reached
Fig 3. Correlation of biopsy point S with histology and semi-automatic cell counting. A) Biopsy point S
is located in the NE area on an axial slide of the intraoperative cT1 MRI. B) Corresponding slice of the 1mm
3
biopsy specimen (HE stain) in x20 magnification which was classified as “viable tumor tissue”. This type of
histology occurred in 89% of all biopsies originating from NE. C) Example of semi-automatic cell counting with
the ImageJ plugin ITCN. Correctly recognized tumor cells are marked red. Yellow dots are falsely detected
areas of apoptotic cells or intercellular space.
doi:10.1371/journal.pone.0169292.g003
Table 1. Biopsy counts by histologic and MRI-based classification.
Histologic classification
Viable tumor cells Necrosis with cellular component Pure necrosis Blood cells
MRI classification NE 92 5 3 3
CE 194 100 3 24
NEC 41 70 5 21
Aχ
2
test revealed a significant relationship between the patterns of histopathologic composition and the classification of the MRI-location in which the
biopsy was found (χ
2
(6) = 92.29, p <0.001).
doi:10.1371/journal.pone.0169292.t001
Glioblastoma Infiltration: Correlation of Histopathology and MRI
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statistical significance (t(319.1) = 4.29, p <0.001 for NECvs. CE/NE, and t(319.6) = 2.90,
p = 0.004 for CEvs. NE).
To account for potential non-normality in the residuals of the dependent variable, the anal-
yses were repeated using non-parametric Mann-Whitney-U tests which replicated the results.
In addition, using the square root transformed cell density as dependent variable in the linear
(-mixed) models also led to comparable results—with the exception that the difference
between NEC and CE/NE for all cells was now also significant in the linear-mixed model.
Discussion
This study illustrates that the appearance of a glioblastoma on a cT1 image (circular enhance-
ment, central necrosis, peritumoral edema) does not strictly correspond to its diffuse histo-
pathological composition. NE had the highest relative content of viable tumor cells and an
Fig 4. Histological composition of the different MRI classifications. For all 561 biopsy samples, the relative frequency of the different
histologic classifications within each MRI classification is displayed. NE = non-enhancing part on cT1; CE = contrast enhancement on cT1;
NEC = Necrosis on cT1.
doi:10.1371/journal.pone.0169292.g004
Glioblastoma Infiltration: Correlation of Histopathology and MRI
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average cellularity comparably high to CE which suggests that considerable infiltration or even
the main tumor burden occur beyond the contrast enhancing part.
If we only take into account biopsies that were classified as “viable tumor cells”, highest cell
density is found in the central parts of the tumor (“MRI necrosis”) and decreases significantly
towards the periphery. It is well established that glioblastomas are disseminated tumors with a
fringe of invasive cells around a core lesion[13] and they have often spread throughout the
brain at initial diagnosis[14,15]. Still, maximum safe resection of the visible lesion is impor-
tant[48], particularly because rapidly migrating cells are thought not to proliferate whereas
proliferating cells tend not to migrate and recurrences usually occur near the area of initial
resection[13].
Fig 5. Boxplots of the cell densities in each MRI compartment. A) Cell densities for all biopsies (“viable tumor cells”, “necrosis with cellular component”,
“pure necrosis”, “blood cells”). B) Cell densities in biopsies with “viable tumor cells” only. NE = non-enhancing part on cT1; CE = contrast enhancement on
cT1; NEC = Necrosis on cT1.
doi:10.1371/journal.pone.0169292.g005
Table 2. Cell densities within MRI classifications.
MRI classification All biopsies Biopsies with “viable tumor cells”
NE 3764 ±2893 4130 ±2817
CE 3506 ±3116 4495 ±3209
NEC 2713 ±3239 5804 ±3480
Mean cell densities (+ standard deviation) [cells/mm
2
] within the different MRI classifications separately for all cells and for biopsies with “viable tumor cells”
only.
doi:10.1371/journal.pone.0169292.t002
Glioblastoma Infiltration: Correlation of Histopathology and MRI
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Other studies are in accordance with our findings, although the proposed problem and the
methodic approach were different.
Kubben et al[16] examined 10 patients (36 biopsy samples) with glioblastoma in an intrao-
perative contrast enhanced 0.15 Tesla MRI after the first resection of the tumor and obtained
neuronavigation-guided biopsies at the border of the resection cavity together with a screen
capture to relate contrast enhancement with histopathology. Other than stating that contrast
enhancement after excision is due to residual tumor rather than iatrogenic tissue manipula-
tion, they found a correlation between the presence of contrast enhancement and increased
cellularity (Kendall’s tau T
k
= 0.26), necrosis (T
k
= 0.49), vascular changes (T
k
= 0.53) and
WHO grade (T
k
= 0.5) in the biopsy sample, but did not compare it to values from NE regions.
The resolution of the 0.15 Tesla scanner that Kubben et al used is very low, which is also a limi-
tation of the study of Kelly et al[17] on 40 patients with brain tumors of different entities. 195
biopsy samples were compared to CT and unenhanced 0.15 Tesla MRI images. They found
that tumor cell infiltration extended at least as far as was indicated by the prolongation of T2
on the MRI but were unable to differentiate edematous from infiltrated parenchyma according
to the prolongation of T1 and T2.
There are three studies that state a higher cellularity in CE compared to NE.
In an analysis of 30 samples (13 patients) from preplanned 75mm
2
region of interests, Bara-
jas et al[18] found that CE biopsy sites had significantly elevated overall cellularity compared
with peritumoral NE biopsy regions (p <0.01). This is a result of manual quantification of the
biopsies as the average of the total number of cells within three high power fields at a magnifi-
cation of x20 (1.0 mm
2
).
In a more recent study[19], the same research group took biopsies during glioblastoma
resection on preselected sites within or outside the contrast enhancement on the preoperative
MRI. They biopsied if the Apparent Diffusion Coefficient (ADC) value <1200, relative peak
height from DSC MRI >3 or choline to N-acetyl aspartate index (CNI) >2 SEs above normal.
Tissue specimens obtained from CE regions had significantly higher tumor cell density com-
pared to NE regions (tumor cell density mean 268 vs. 146 total cells per field at x200 magnifica-
tion, p = 0.007).
Gill et al[20] performed an analysis of MRI-localized biopsies of 69 glioblastoma patients
with an emphasis on the composite gene expression profile within enhancing and non-
enhancing parts of a glioblastoma. They also found a significantly higher cellularity in samples
from CE compared to NE regions.
Our results on the other hand suggest that the cell density within CE and NE is generally
comparable (however, there was a statistically significant difference if only biopsies with
entirely tumorous appearance were considered in a linear-mixed model). Interestingly, the
proportion of “viable tumor cells” in NE in our study is higher than in CE, which is inter-
spersed with necrosis. This indicates that the main burden of proliferative tumor cells is
located in NE. Hence, adding a qualitative category to the analysis of the biopsies instead of
merely counting the cells provides a better understanding of what the glioblastoma must look
like in the different MRI locations. In this regard, necrosis was present in 40% of CE samples
in the study of Barajas et al, which is in accordance with our results.
The stated discrepancy in regard to the cell densities in CE and NE might be explained as
all three studies performed a manual cell count that does not achieve the same accuracy as
semi-automatic cell counting. As only distinct high-power-fields are chosen for counting, the
approach is also observer-dependent. The only limitation of semi-automatic cell counting on
the other hand is that they do not account for signs of malignancy in cells such as cytologic aty-
pia, enlarged nuclear to cytoplasmic volume ratio or hyperchromasia. The algorithm was
trained to detect tumorous cells and in most cases was able to distinguish these from benign or
Glioblastoma Infiltration: Correlation of Histopathology and MRI
PLOS ONE | DOI:10.1371/journal.pone.0169292 January 19, 2017 9 / 12
apoptotic brain cells due to slight differences in cell width and spacing. However, it cannot be
guaranteed that some of the latter cells have been falsely detected. As only HE stains were avail-
able, differentiation of aforementioned histological features was limited. Furthermore, the
decision to apply categories to the histologic classification might narrow the content of infor-
mation that can be obtained from the biopsies, but it allows us to focus on distinguishing the
infiltrative tumor parts (“viable tumor cells”) from the rest of the tumor mass (“blood cells”,
“pure necrosis”, “necrosis with cellular component”). With every classification, there is also a
certain observer dependency, which we tried to minimize by consensus reading of a neurora-
diologist and a neuropathologist.
In regard to the studies of Barajas et al. [18,19], the pre-selection of biopsy sites by their
functional MR values (ADC, Cerebral Blood Volume (CBV)) may have been a possible con-
founder, as the ADC itself is reported to be inversely correlated with cellularity [21] and CBV
might show a trend to a positive correlation [22].
Another limitation of our study is that NE and CE biopsy regions were in nearly all cases
adjacent. This technical limitation which is caused by the direction of the stereotactic biopsy
trajectory might constitute a further element of bias.
A further limitation of this study is that exclusively T1-weighted intraoperative images were
included, as it is well known that T2-hyperintense areas include invasive parts of glioblastoma
[23,24]. However, T2-hyperintensities may also be caused by multiple confounding condi-
tions (e.g. edema, demyelination, ischemic injury, seizures) [3]. Hence, in future studies it
may be of interest to perform the histopathological correlational analysis on the basis of T2-
weighted images.
The strengths of our study are the high resolution of the 1.5 Tesla MRI and the large sample
of 561 biopsies, exclusively obtained from glioblastoma patients. Also, the allocation of the
biopsies to the MRI is precise and observer-independent in our approach.
From a clinical point of view, our study might advocate larger margins for resection or radi-
ation therapy for patients with newly diagnosed glioblastoma. However, no firm conclusions
can be drawn from the current correlational study and future carefully designed studies—out-
weighing risk and benefit for the patient—might address such an approach.
In conclusion, contrast enhanced T1-weighted MRI cannot reliably distinguish necrotic
foci from areas of infiltrative tumor with high cellularity. The non-enhancing part on the cT1
MRI (NE) seems to correspond best to the zone of tumor infiltration and contains a high
tumor burden which is why a resection margin that exceeds the widely used contrast enhance-
ment on cT1 has to be considered in the excision of glioblastoma. This finding is also impor-
tant for studies that correlate MRI parameters with tumor biology on a region of interest-
based approach.
Acknowledgments
S. Burth is supported by the Mildred-Scheel-Doktorandenprogramm of the German Cancer
Aid, grant number 111583.
Author Contributions
Conceptualization: OE AR.
Data curation: OE.
Formal analysis: OE PJK AR.
Investigation: OE AR.
Glioblastoma Infiltration: Correlation of Histopathology and MRI
PLOS ONE | DOI:10.1371/journal.pone.0169292 January 19, 2017 10 / 12
Methodology: OE.
Project administration: AR.
Resources: JON CJ KK AU.
Software: OE.
Supervision: AR.
Validation: OE AR.
Visualization: OE.
Writing original draft: OE S. Burth AR.
Writing review & editing: OE S. Burth JON PJK FS CJ PK S. Bickelhaupt SM PB WW HPS
KK AU MB AR.
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Glioblastoma Infiltration: Correlation of Histopathology and MRI
PLOS ONE | DOI:10.1371/journal.pone.0169292 January 19, 2017 12 / 12
... This supports the hypothesis that the viable tumor volume with possibly densely packed cells is assumed to be at the edges of the tumor volume. 74 On the other hand, the central parts of the tumor, which were largely hypointense or mildly contrast enhancing in the T1w Gd images, exhibited mainly negative or near zero TDD contrast, as was seen for example, in Patient F ( Figure S5). This is in line with the assumption that tumor cores have either moderate cellularity or are necrotic. ...
... This is in line with the assumption that tumor cores have either moderate cellularity or are necrotic. 74 Based on the simulations, it was clear that the TDD contrast is lowered by lower cell densities and higher diffusional exchange, both of which might be more prominent in the necrotic tumor core than in the viable edges. 74,75 Finally, the TDD contrast maps seemed to be able to distinguish purely vasogenic edema from potential infiltrative edema (Patient A) and from potential ischemia (Patient C). ...
... 74 Based on the simulations, it was clear that the TDD contrast is lowered by lower cell densities and higher diffusional exchange, both of which might be more prominent in the necrotic tumor core than in the viable edges. 74,75 Finally, the TDD contrast maps seemed to be able to distinguish purely vasogenic edema from potential infiltrative edema (Patient A) and from potential ischemia (Patient C). ...
Article
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Background Quantitative imaging biomarkers (QIBs) can characterize tumor heterogeneity and provide information for biological guidance in radiotherapy (RT). Time‐dependent diffusion MRI (TDD‐MRI) derived parameters are promising QIBs, as they describe tissue microstructure with more specificity than traditional diffusion‐weighted MRI (DW‐MRI). Specifically, TDD‐MRI can provide information about both restricted diffusion and diffusional exchange, which are the two time‐dependent effects affecting diffusion in tissue, and relevant in tumors. However, exhaustive modeling of both effects can require long acquisitions and complex model fitting. Furthermore, several introduced TDD‐MRI measurements can require high gradient strengths and/or complex gradient waveforms that are possibly not available in RT settings. Purpose In this study, we investigated the feasibility of a simple analysis framework for the detection of restricted diffusion and diffusional exchange effects in the TDD‐MRI signal. To promote the clinical applicability, we use standard gradient waveforms on a conventional 1.5 T MRI system with moderate gradient strength (Gmax = 45 mT/m), and on a hybrid 1.5 T MRI‐Linac system with low gradient strength (Gmax = 15 mT/m). Methods Restricted diffusion and diffusional exchange were simulated in geometries mimicking tumor microstructure to investigate the DW‐MRI signal behavior and to determine optimal experimental parameters. TDD‐MRI was implemented using pulsed field gradient spin echo with the optimized parameters on a conventional MRI system and a MRI‐Linac. Experiments in green asparagus and 10 patients with brain lesions were performed to evaluate the time‐dependent diffusion (TDD) contrast in the source DW‐images. Results Simulations demonstrated how the TDD contrast was able to differentiate only dominating diffusional exchange in smaller cells from dominating restricted diffusion in larger cells. The maximal TDD contrast in simulations with typical cancer cell sizes and in asparagus measurements exceeded 5% on the conventional MRI but remained below 5% on the MRI‐Linac. In particular, the simulated TDD contrast in typical cancer cell sizes (r = 5–10 µm) remained below or around 2% with the MRI‐Linac gradient strength. In patients measured with the conventional MRI, we found sub‐regions reflecting either dominating restricted diffusion or dominating diffusional exchange in and around brain lesions compared to the noisy appearing white matter. Conclusions On the conventional MRI system, the TDD contrast maps showed consistent tumor sub‐regions indicating different dominating TDD effects, potentially providing information on the spatial tumor heterogeneity. On the MRI‐Linac, the available TDD contrast measured in asparagus showed the same trends as with the conventional MRI but remained close to typical measurement noise levels when simulated in common cancer cell sizes. On conventional MRI systems with moderate gradient strengths, the TDD contrast could potentially be used as a tool to identify which time‐dependent effects to include when choosing a biophysical model for more specific tumor characterization.
... The selection of unlabeled tumoral samples and normal brain samples was based on multifold considerations: (a) Representation of tumoral heterogeneity: Biologically, a GBM tumor includes a contrast-enhancing portion (CE) and a non-enhancing portion (NE). The former harbors proliferative tumor cells, while the latter harbors invading tumor to the surrounding brain tissue [28]. To ensure our unlabeled samples capture this biological heterogeneity of each tumor, an equal number of samples were taken from CE and NE. ...
... (b) Avoidance of outlier samples: We were careful to avoid selecting samples from areas that could be considered outliers. Notably, we excluded regions like necrosis, where the tissue characteristics significantly differ [28]. Additionally, for tumors located near fixed brain structures like the skull or cerebrospinal fluid, precautions were taken to prevent sample overlap with these structures. ...
Article
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Background and objective Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome. Methods We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. Results WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes. Conclusions This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.
... Tumour infiltration is known to extend beyond regions of contrast enhancement and dense, viable tumour is identified in non-enhancing regions 6,55 . Our findings of poor sensitivity are consistent with previous radiopathologic correlation studies 65,66 . 5-ALA fluorescence is used in glioblastoma surgery to guide surgical resection 28 . ...
Article
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A critical challenge in glioma treatment is detecting tumour infiltration during surgery to achieve safe maximal resection1, 2–3. Unfortunately, safely resectable residual tumour is found in the majority of patients with glioma after surgery, causing early recurrence and decreased survival4, 5–6. Here we present FastGlioma, a visual foundation model for fast (<10 s) and accurate detection of glioma infiltration in fresh, unprocessed surgical tissue. FastGlioma was pretrained using large-scale self-supervision (around 4 million images) on rapid, label-free optical microscopy, and fine-tuned to output a normalized score that indicates the degree of tumour infiltration within whole-slide optical images. In a prospective, multicentre, international testing cohort of patients with diffuse glioma (n = 220), FastGlioma was able to detect and quantify the degree of tumour infiltration with an average area under the receiver operating characteristic curve of 92.1 ± 0.9%. FastGlioma outperformed image-guided and fluorescence-guided adjuncts for detecting tumour infiltration during surgery by a wide margin in a head-to-head, prospective study (n = 129). The performance of FastGlioma remained high across diverse patient demographics, medical centres and diffuse glioma molecular subtypes as defined by the World Health Organization. FastGlioma shows zero-shot generalization to other adult and paediatric brain tumour diagnoses, demonstrating the potential for our foundation model to be used as a general-purpose adjunct for guiding brain tumour surgeries. These findings represent the transformative potential of medical foundation models to unlock the role of artificial intelligence in the care of patients with cancer.
... GBM is characterized by invasive growth, and typically shows no discernible boundaries with the surrounding normal brain tissue. In the PTBE region is difficult to accurately distinguish tumor boundary, and the presence of scattered tumor cells increases the possibility of tumor recurrence after operation (6)(7)(8). While the use of CT, MRI, and other imaging methods for studying tumor morphology in relation to tumor prognosis has been long reported, a unified morphological evaluation index for measuring brain tumor size and edema degree is still lacking. ...
Article
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Background Peritumoral brain edema (PTBE) represents a characteristic phenotype of intracranial gliomas. However, there is a lack of consensus regarding the prognosis and mechanism of PTBE. In this study, clinical imaging data, along with publicly available imaging data, were utilized to assess the prognosis of PTBE in glioblastoma (GBM) patients, and the associated mechanisms were preliminarily analyzed. Methods We investigated relevant imaging features, including edema, in GBM patients using ITK-SNAP imaging segmentation software. Risk factors affecting progression-free survival (PFS) and overall survival (OS) were assessed using a Cox proportional hazard regression model. In addition, the impact of PTBE on PFS and OS was analyzed in clinical GBM patients using the Kaplan–Meier survival analysis method, and the results further validated by combining data from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Finally, functional enrichment analysis based on TCIA and TCGA datasets identified several pathways potentially involved in the mechanism of edema formation. Results This study included a total of 32 clinical GBM patients and 132 GBM patients from public databases. Univariate and multivariate analyses indicated that age and edema index (EI) are independent risk factors for PFS, but not for OS. Kaplan–Meier curves revealed consistent survival analysis results between IE groups among both clinical patients and TCIA and TCGA patients, suggesting a significant effect of PTBE on PFS but not on OS. Furthermore, functional enrichment analysis predicted the involvement of several pathways related mainly to cellular bioenergetics and vasculogenic processes in the mechanism of PTBE formation. While these novel results warrant confirmation in a larger patient cohort, they support good prognostic value for PTBE assessment in GBM. Conclusions Our results indicate that a low EI positively impacts disease control in GBM patients, but this does not entirely translate into an improvement in OS. Multiple genes, signaling pathways, and biological processes may contribute to the formation of peritumoral edema in GBM through cytotoxic and vascular mechanisms.
... However, the lack of significance warrants a cautious interpretation. After GTR, the surgical cavity and surrounding tissues may harbor residual microscopic glioma cells not visible on imaging [19]. The less conformal nature of 3D-CRT may cover these regions more effectively, despite the potential for slightly increased toxicity [8,9]. ...
Article
Purpose: To evaluate recurrence patterns of and survival outcomes in glioblastoma treated with intensity-modulated radiation therapy (IMRT) versus three-dimensional conformal radiation therapy (3D-CRT). Materials and methods: We retrospectively examined 91 patients with glioblastoma treated with either IMRT (n = 60) or 3D-CRT (n = 31) between January 2013 and December 2019. Magnetic resonance imaging showing tumor recurrence and planning computed tomography scans were fused for analyzing recurrence patterns categorized as in-field, marginal, and out-of-field based on their relation to the initial radiation field. Results: The median overall survival (OS) was 18.9 months, with no significant difference between the groups. The median progression-free survival (PFS) was 9.4 months, with no significant difference between the groups. Patients who underwent gross total resection (GTR) had higher OS and PFS than those who underwent less extensive surgery. Among 78 relapse cases, 67 were of in-field; 5, marginal; and 19, out-of-field recurrence. Among 3D-CRT-treated cases, 24 were of in-field; 1, marginal; and 9, out-of-field recurrence. Among IMRT-treated cases, 43 were of in-field; 4, marginal; and 10, out-of-field recurrence. In partial tumor removal or biopsy cases, out-of-field recurrence was less frequent in the IMRT (16.2%) than in the 3D-CRT (36.3%) group, with marginal significance (p = 0.079). Conclusion: IMRT and 3D-CRT effectively managed glioblastoma with no significant differences in OS and PFS. The survival benefit with GTR underscored the importance of maximal surgical resection. The reduced rate of out-of-field recurrence in IMRT-treated patients with partial resection highlights its potential utility in cases with unfeasible complete tumor removal.
... For instance, modern antibodybased treatments that have improved the outcomes of many solid tumors do not cross the BBB 1 . Indeed, GBM cells are known to migrate and infiltrate brain regions well beyond what is revealed on magnetic resonance imaging (MRI) by contrast uptake where the BBB is impermeable to several systemically delivered agents 2,3 . Even when the enhancing tumor region is completely resected, infiltrating residual tumor cells lead to recurrence, with patients almost invariably succumbing to their disease 4,5 . ...
Article
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Given the marginal penetration of most drugs across the blood-brain barrier, the efficacy of various agents remains limited for glioblastoma (GBM). Here we employ low-intensity pulsed ultrasound (LIPU) and intravenously administered microbubbles (MB) to open the blood-brain barrier and increase the concentration of liposomal doxorubicin and PD-1 blocking antibodies (aPD-1). We report results on a cohort of 4 GBM patients and preclinical models treated with this approach. LIPU/MB increases the concentration of doxorubicin by 2-fold and 3.9-fold in the human and murine brains two days after sonication, respectively. Similarly, LIPU/MB-mediated blood-brain barrier disruption leads to a 6-fold and a 2-fold increase in aPD-1 concentrations in murine brains and peritumoral brain regions from GBM patients treated with pembrolizumab, respectively. Doxorubicin and aPD-1 delivered with LIPU/MB upregulate major histocompatibility complex (MHC) class I and II in tumor cells. Increased brain concentrations of doxorubicin achieved by LIPU/MB elicit IFN-γ and MHC class I expression in microglia and macrophages. Doxorubicin and aPD-1 delivered with LIPU/MB results in the long-term survival of most glioma-bearing mice, which rely on myeloid cells and lymphocytes for their efficacy. Overall, this translational study supports the utility of LIPU/MB to potentiate the antitumoral activities of doxorubicin and aPD-1 for GBM.
... However, the latter results raise again the question of the significance of the FLAIR-hyperintense disease; on one hand, the cytoreductive effect of resecting the non-CE areas surrounding the main core of GBM is evident by all the aforementioned studies and our own data; Eidel et al. [3] also have demonstrated that the highest content of viable malignant cells was present within the FLAIR-TV rather than in the CE-TV or necrotic components. On the other hand, if a molecular characteristic such as MGMT, already known as a strong predictor of response to post-surgical therapies [6,15,17], does influence the effect of EOR on survival, such significance may lie beyond cytoreduction, bringing the biological nature of FLAIR disease under the spotlight. ...
Article
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Background The prognostic value of the extent of resection in the management of Glioblastoma is a long-debated topic, recently widened by the 2022 RANO-Resect Classification, which advocates for the resection of the non-enhancing disease surrounding the main core of tumors (supramaximal resection, SUPR) to achieve additional survival benefits. We conducted a retrospective analysis to corroborate the role of SUPR by the RANO-Resect Classification in a single center, homogenous cohort of patients. Methods Records of patients operated for WHO-2021 Glioblastomas at our institution between 2007 and 2018 were retrospectively reviewed; volumetric data of resected lesions were computed and classified by RANO-Resect criteria. Survival and correlation analyses were conducted excluding patients below near-total resection. Results 117 patients met the inclusion criteria, encompassing 45 near-total resections (NTR), 31 complete resections (CR), and 41 SUPR. Median progression-free and overall survival were 11 and 15 months for NTR, 13 and 17 months or CR, 20 and 24 months for SUPR, respectively (p < 0.001), with inverse correlation observed between survival and FLAIR residual volume (r -0.28). SUPR was not significantly associated with larger preoperative volumes or higher rates of postoperative deficits, although it was less associated with preoperative neurological deficits (OR 3.37, p = 0.003). The impact of SUPR on OS varied between MGMT unmethylated (HR 0.606, p = 0.044) and methylated (HR 0.273, p = 0.002) patient groups. Conclusions Results of the present study support the validity of supramaximal resection by the new RANO-Resect classification, also highlighting a possible surgical difference between tumors with methylated and unmethylated MGMT promoter.
... Radiomic markers have been investigated within the area of peritumoral edema, and a radiomic signature of infiltration in peritumoral edema has been discovered, which can predict subsequent recurrence in glioblastoma [31,32]. Moreover, it has been established that the non-enhanced subregion of the tumor is just as cellular as the enhanced subregion [33]. Patients who undergo resection of both the non-enhanced and enhanced subregions appear to have a better prognosis [34]. ...
Article
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Background: Extracting multiregional radiomic features from multiparametric MRI for predicting pretreatment survival in isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) patients is a promising approach. Methods: MRI data from 49 IDH wild-type glioblastoma patients pre-treatment were utilized. Diffusion and perfusion maps were generated, and tumor subregions segmented. Radiomic features were extracted for each tissue type and map. Feature selection on 1862 radiomic features identified 25 significant features. The Cox proportional-hazards model with LASSO regularization was used to perform survival analysis. Internal and external validation used a 38-patient training cohort and an 11-patient validation cohort. Statistical significance was set at p < 0.05. Results: Age and six radiomic features (shape and first and second order) from T1W, diffusion, and perfusion maps contributed to the final model. Findings suggest that a small necrotic subregion, inhomogeneous vascularization in the solid non-enhancing subregion, and edema-related tissue damage in the enhancing and edema subregions are linked to poor survival. The model’s C-Index was 0.66 (95% C.I. 0.54–0.80). External validation demonstrated good accuracy (AUC > 0.65) at all time points. Conclusions: Radiomics analysis, utilizing segmented perfusion and diffusion maps, provide predictive indicators of survival in IDH wild-type glioblastoma patients, revealing associations with microstructural and vascular heterogeneity in the tumor.
Article
Background and purpose Despite multimodal treatment of glioblastoma (GBM), recurrence beyond the initial tumor volume is inevitable. Moreover, conventional MRI has shortcomings that hinder the early detection of occult white matter tract infiltration by tumor, but diffusion tensor imaging (DTI) is a sensitive probe for assessing microstructural changes, facilitating the identification of progression before standard imaging. This sensitivity makes DTI a valuable tool for predicting recurrence. A systematic review was therefore conducted to investigate how DTI, in comparison to conventional MRI, can be used for predicting GBM progression. Methods We queried three databases (PubMed, Web of Science, and Scopus) using the search terms: (diffusion tensor imaging OR DTI) AND (glioblastoma OR GBM) AND (recurrence OR progression). For included studies, data pertaining to the study type, number of GBM recurrence patients, treatment type(s), and DTI‐related metrics of recurrence were extracted. Results In all, 16 studies were included, from which there were 394 patients in total. Six studies reported decreased fractional anisotropy in recurrence regions, and 2 studies described the utility of connectomics/tractography for predicting tumor migratory pathways to a site of recurrence. Three studies reported evidence of tumor progression using DTI before recurrence was visible on conventional imaging. Conclusions These findings suggest that DTI metrics may be useful for guiding surgical and radiotherapy planning for GBM patients, and for informing long‐term surveillance. Understanding the current state of the literature pertaining to these metrics’ trends is crucial, particularly as DTI is increasingly used as a treatment‐guiding imaging modality.
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Objective: Several studies have analyzed a correlation between the apparent diffusion coefficient (ADC) derived from diffusion-weighted MRI and the tumor cellularity of corresponding histopathological specimens in brain tumors with inconclusive findings. Here, we compared a large dataset of ADC and cellularity values of stereotactic biopsies of glioblastoma patients using a new postprocessing approach including trajectory analysis and automatic nuclei counting. Materials and methods: Thirty-seven patients with newly diagnosed glioblastomas were enrolled in this study. ADC maps were acquired preoperatively at 3T and coregistered to the intraoperative MRI that contained the coordinates of the biopsy trajectory. 561 biopsy specimens were obtained; corresponding cellularity was calculated by semi-automatic nuclei counting and correlated to the respective preoperative ADC values along the stereotactic biopsy trajectory which included areas of T1-contrast-enhancement and necrosis. Results: There was a weak to moderate inverse correlation between ADC and cellularity in glioblastomas that varied depending on the approach towards statistical analysis: for mean values per patient, Spearman's ρ = -0.48 (p = 0.002), for all trajectory values in one joint analysis Spearman's ρ = -0.32 (p < 0.001). The inverse correlation was additionally verified by a linear mixed model. Conclusions: Our data confirms a previously reported inverse correlation between ADC and tumor cellularity. However, the correlation in the current article is weaker than the pooled correlation of comparable previous studies. Hence, besides cell density, other factors, such as necrosis and edema might influence ADC values in glioblastomas.
Article
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Significance Molecular analysis of surgically resected glioblastomas (GBM) samples has uncovered phenotypically and clinically distinct tumor subtypes. However, little is known about the molecular features of the glioma margins that are left behind after surgery. To address this key issue, we performed RNA-sequencing (RNA-seq) and histological analysis on MRI-guided biopsies from the contrast-enhancing core and nonenhancing margins of GBM. Computational deconvolution of the RNA-seq data revealed that cellular composition, including nonneoplastic cells, is a major determinant of the expression patterns at the margins of GBM. The different GBM subtypes show distinct expression patterns that relate the contrast enhancing centers to the nonenhancing margins of tumors. Understanding these patterns may provide a means to infer the molecular and cellular features of residual disease.
Article
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To perform a meta-analysis exploring the correlation between the apparent diffusion coefficient (ADC) and tumor cellularity in patients. We searched medical and scientific literature databases for studies discussing the correlation between the ADC and tumor cellularity in patients. Only studies that were published in English or Chinese prior to November 2012 were considered for inclusion. Summary correlation coefficient (r) values were extracted from each study, and 95% confidence intervals (CIs) were calculated. Sensitivity and subgroup analyses were performed to investigate potential heterogeneity. Of 189 studies, 28 were included in the meta-analysis, comprising 729 patients. The pooled r for all studies was -0.57 (95% CI: -0.62, -0.52), indicating notable heterogeneity (P<0.001). After the sensitivity analysis, two studies were excluded, and the pooled r was -0.61 (95% CI: -0.66, -0.56) and was not significantly heterogeneous (P = 0.127). Regarding tumor type subgroup analysis, there were sufficient data to support a strong negative correlation between the ADC and cellularity for brain tumors. There was no notable evidence of publication bias. There is a strong negative correlation between the ADC and tumor cellularity in patients, particularly in the brain. However, larger, prospective studies are warranted to validate these findings in other cancer types.
Article
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Background Digital pathology images are increasingly used both for diagnosis and research, because slide scanners are nowadays broadly available and because the quantitative study of these images yields new insights in systems biology. However, such virtual slides build up a technical challenge since the images occupy often several gigabytes and cannot be fully opened in a computer’s memory. Moreover, there is no standard format. Therefore, most common open source tools such as ImageJ fail at treating them, and the others require expensive hardware while still being prohibitively slow. Results We have developed several cross-platform open source software tools to overcome these limitations. The NDPITools provide a way to transform microscopy images initially in the loosely supported NDPI format into one or several standard TIFF files, and to create mosaics (division of huge images into small ones, with or without overlap) in various TIFF and JPEG formats. They can be driven through ImageJ plugins. The LargeTIFFTools achieve similar functionality for huge TIFF images which do not fit into RAM. We test the performance of these tools on several digital slides and compare them, when applicable, to standard software. A statistical study of the cells in a tissue sample from an oligodendroglioma was performed on an average laptop computer to demonstrate the efficiency of the tools. Conclusions Our open source software enables dealing with huge images with standard software on average computers. They are cross-platform, independent of proprietary libraries and very modular, allowing them to be used in other open source projects. They have excellent performance in terms of execution speed and RAM requirements. They open promising perspectives both to the clinician who wants to study a single slide and to the research team or data centre who do image analysis of many slides on a computer cluster.
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Objective: There remains no general consensus in the neurosurgical oncology literature regarding the role of extent of glioma resection in improving patient outcome. Although the value of resection in establishing a diagnosis and alleviating mass effect is clear, there is less certainty in ascertaining the influence of extent of resection (EOR). Here, we review the recent literature to synthesize a comprehensive review of the value of extent of resection for gliomas in the modern neurosurgical era. Methods: We reviewed every major peer-reviewed clinical publication since 1990 on the role of EOR in glioma outcome. Results: Thirty-two high-grade glioma articles and 11 low-grade glioma articles were examined in terms of quality of evidence, expected EOR, and survival benefit. Conclusion: Despite limitations in the quality of data, mounting evidence suggests that more extensive surgical resection is associated with longer life expectancy for both low- and high-grade newly diagnosed gliomas.
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For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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According to the Response Assessment in Neurooncology (RANO) criteria, significant nonenhancing signal increase in T2-weighted images qualifies for progression in high-grade glioma (T2-progress), even if there is no change in the contrast-enhancing tumor portion. The purpose of this retrospective study was to assess the frequency of isolated T2-progress and its predictive value on subsequent T1-progress, as determined by a T2 signal increase of 15% or 25%, respectively. The frequency of T2-progress was correlated with antiangiogenic therapy. MRI follow-up examinations (n = 777) of 144 patients with histologically proven glioblastoma were assessed for contrast-enhanced T1 and T2-weighted images. Examinations were classified as T1-progress, T2-progress with 15% or 25% T2-signal increase, stable disease, or partial or complete response. Thirty-five examinations revealed exclusive T2-progress using the 15% criterion, and only 2 examinations qualified for the 25% criterion; 61.8% of the scans presenting T2-progress and 31.5% of the scans presenting stable disease revealed T1-progress in the next follow-up examination. The χ(2) test showed a highly significant correlation (P < .001) between T2-progress, with the 15% criterion and subsequent T1-progress. No correlation between antiangiogenic therapy and T2-progress was shown. Tumor progression, as determined by both contrast-enhanced T1 and T2 sequences is more frequently diagnosed than when considering only contrast-enhanced T1 sequences. Definition of T2-progress by a 15% T2-signal increase criterion is superior to a 25% criterion. The missing correlation of T2-progress and antiangiogenic therapy supports the hypothesis of T2-progress as part of the natural course of the tumor disease.
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