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Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data


Abstract and Figures

Colorectal cancer (CRC) is third in prevalence and mortality among all cancers in the US. Currently, the United States Preventative Services Task Force (USPSTF) recommends anyone ages 50-75 and/or with a family history to be screened for CRC. To improve screening specificity and sensitivity, we have built an artificial neural network (ANN) trained on 12 to 14 categories of personal health data from the National Health Interview Survey (NHIS). Years 1997-2016 of the NHIS contain 583,770 respondents who had never received a diagnosis of any cancer and 1409 who had received a diagnosis of CRC within 4 years of taking the survey. The trained ANN has sensitivity of 0.57 ± 0.03, specificity of 0.89 ± 0.02, positive predictive value of 0.0075 ± 0.0003, negative predictive value of 0.999 ± 0.001, and concordance of 0.80 ± 0.05 per the guidelines of Transparent Reporting of Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) level 2a, comparable to current risk-scoring methods. To demonstrate clinical applicability, both USPSTF guidelines and the trained ANN are used to stratify respondents to the 2017 NHIS into low-, medium- and high-risk categories (TRIPOD levels 4 and 2b, respectively). The number of CRC respondents misclassified as low risk is decreased from 35% by screening guidelines to 5% by ANN (in 60 cases). The number of non-CRC respondents misclassified as high risk is decreased from 53% by screening guidelines to 6% by ANN (in 25,457 cases). Our results demonstrate a robustly-tested method of stratifying CRC risk that is non-invasive, cost-effective, and easy to implement publicly.
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Scoring colorectal cancer risk with an artificial
neural network based on self-reportable
personal health data
Bradley J. NartowtID
, Gregory R. Hart
, David A. Roffman
, Xavier Llor
, Issa Ali
Wazir MuhammadID
, Ying Liang
, Jun Deng
1Department of Therapeutic Radiology, School of Medicine, Yale University, New Haven, Connecticut,
United States of America, 2Sun Nuclear Corporation, Melbourne, FL, United States of America,
3Department of Digestive Diseases, School of Medicine, Yale University, New Haven, Connecticut, United
States of America
Colorectal cancer (CRC) is third in prevalence and mortality among all cancers in the US.
Currently, the United States Preventative Services Task Force (USPSTF) recommends
anyone ages 50–75 and/or with a family history to be screened for CRC. To improve screen-
ing specificity and sensitivity, we have built an artificial neural network (ANN) trained on 12
to 14 categories of personal health data from the National Health Interview Survey (NHIS).
Years 1997–2016 of the NHIS contain 583,770 respondents who had never received a diag-
nosis of any cancer and 1409 who had received a diagnosis of CRC within 4 years of taking
the survey. The trained ANN has sensitivity of 0.57 ±0.03, specificity of 0.89 ±0.02, positive
predictive value of 0.0075 ±0.0003, negative predictive value of 0.999 ±0.001, and concor-
dance of 0.80 ±0.05 per the guidelines of Transparent Reporting of Multivariable Prediction
Model for Individual Prognosis or Diagnosis (TRIPOD) level 2a, comparable to current risk-
scoring methods. To demonstrate clinical applicability, both USPSTF guidelines and the
trained ANN are used to stratify respondents to the 2017 NHIS into low-, medium- and high-
risk categories (TRIPOD levels 4 and 2b, respectively). The number of CRC respondents
misclassified as low risk is decreased from 35% by screening guidelines to 5% by ANN (in
60 cases). The number of non-CRC respondents misclassified as high risk is decreased
from 53% by screening guidelines to 6% by ANN (in 25,457 cases). Our results demonstrate
a robustly-tested method of stratifying CRC risk that is non-invasive, cost-effective, and
easy to implement publicly.
Colorectal adenocarcinomas are the result of unregulated growth in the colon mucosa that
commonly starts with polypoid lesions progressing into advanced cancers [1]. Of all new can-
cer cases in the US, 8.0% are colorectal. Colorectal cancer (CRC) claims 8.4% of all cancer-
deaths and the overall 5-year survival rate is 66% [2]. Early stage (localized) CRC has a 5-year
PLOS ONE | August 22, 2019 1 / 18
Citation: Nartowt BJ, Hart GR, Roffman DA, Llor X,
Ali I, Muhammad W, et al. (2019) Scoring
colorectal cancer risk with an artificial neural
network based on self-reportable personal health
data. PLoS ONE 14(8): e0221421.
Editor: Frank T. Kolligs, University of Munich,
Received: March 5, 2019
Accepted: August 6, 2019
Published: August 22, 2019
Copyright: ©2019 Nartowt 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: All data used in this
paper is publicly available through the CDC’s
website. At the time of submission the URL:
questionnaires-documentation.htm, goes directly
to the webpage from which each year of NHIS data
can be found.
Funding: Research reported in this publication was
solely supported by the National Institute of
Biomedical Imaging and Bioengineering of the
National Institutes of Health under Award Number
survival rate close to 90%, while that of distant/metastatic CRC survival is less than 14% [2].
Treatment of colorectal cancer even in its metastatic state is fairly standardized, typically with
bevacizumab [3] which has a side-effect of high blood pressure dependent upon the dose [4,5].
There are multiple personal health factors correlating moderately with incidence of
advanced colorectal neoplasia [6] and colorectal cancer which are both self-reportable, easily
gathered, and usable in scoring CRC risk [710]. For example, CRC is more frequent in men
than women and African Americans have the highest incidence in the US [2,11]. Environmen-
tal factors, socio-economic features, and co-morbidities additionally influence CRC risk [2]. In
a study of subjects from Wisconsin and Minnesota, risk factors for long-term colorectal-cancer
mortality were found to be age, sex, and higher body-mass index (BMI) [12], though colorec-
tal-cancer incidence was only marginally increased with higher BMI. In a meta-study more
specialized to BMI as a factor of CRC risk, those of lower BMI were at risk for colorectal cancer
as well [13]. It is possible to build a CRC risk score from these many factors, and many authors
have done so [1416].
Efforts to create risk indexes or prediction models for CRC [1416] have used a variety of
data of three types: (1) routine, (2) reportable by self-completed questionnaire, (3) genetic bio-
markers as recently summarized [7,17]. While logistic regression [18] is a popular method of
scoring CRC risk [15], this work opts to use an artificial neural network (ANN) trained with
professionally-collected routine data [19] and shown in Fig 1. While an ANN is not strictly
superior to logistic regression [20], an ANN incorporates complicated inter-factor coupling
[21]. Given the complexity of human biology, inter-factor coupling is likely to be important in
the predicting CRC from personal health data.
The United States Preventative Services Task Force [22] (USPSTF) and various medical
societies [23] currently recommend screening by age and family history only, despite models
incorporating additional risk factors. Specifically, those with no family history of cancer aged
50–75 are recommended for screening [22,24], meaning that United States citizens living
to age 50 and beyond are flagged for screening. Of the incidences of CRC in the National
Health Interview Survey (NHIS) dataset [19] within 4 years of the survey, 65.6% occur in ages
50–75 and 80.7% are ages 50 and older, leaving 19.3% under age 50 and outside the USPSTF
Fig 1. Schematic of example ANN. A schematic of an ANN with four layers and a logistic activation function. The
ANN in this paper has one input neuron for each of the 12 to 14 factors, the same numberof neurons in each hidden
layer, and a single output neuron for the prediction. The upper arrow indicates forward-propagation and the lower
arrow indicates back-propagation.
Scoring colorectal cancer risk based on personal health data
PLOS ONE | August 22, 2019 2 / 18
R01EB022589. DAR was initially funded by
R01EB022589 when writing the first working
version of the ANN. BJN et al. took over and
continued this study with the funding of
R01EB022589 while DAR went on to be employed
and supported with salary by Sun Nuclear
Corporation. Sun Nuclear Corporation provided
support in the form of salaries for authors DAR,
but did not have any additional role in the study
design, data collection and analysis, decision to
publish, or preparation of the manuscript. The
specific roles of these authors are articulated in the
‘author contributions’ section. The specific role of
DAR is articulated in the ‘author contributions’
section. The content is solely the responsibility of
the authors and does not necessarily represent the
official views of the National Institutes of Health or
of Sun Nuclear Corporation.
Competing interests: I have read the journal’s
policy and the authors of this manuscript have the
following competing interests: DAR was employed
and supported with salary by Sun Nuclear
Corporation. This does not alter our adherence to
PLOS ONE policies on sharing data and materials.
screening guidelines. The USPSTF’s screening guidelines saves many lives [25] but at the
expense of many false-positives, which could lead to unnecessary, expensive, and occasionally
injurious screening. There is also a remainder of false-negatives (specifically, CRC occurring
under age 50 in those with no family history [11]) that could be flagged for screening by an
appropriate model of risk.
Current screening procedures for CRC (by colonoscopy [24,26,27] every 10 years or sig-
moidoscopy [28,29] / colonography [22] every 5 years) are often invasive with suboptimal
accuracy, and always expensive. For example, perforation of the colon and/or bleeding during
both colonoscopy and flexible sigmoidoscopy have been reported, demonstrating the need for
non-invasive techniques [22]. Hence, even at the expense of lowered positive/negative predic-
tive value (PPV/NPV), there has been a push for less expensive and less invasive screening
methods. From the 1990s to the 2010s, this push has yielded the yearly fecal occult blood test
(FOBT) [29] and the yearly fecal immunochemical test (FIT) [30] possibly combined [31] with
the SEPT9-methylation [32,33] test. An efficient and non-invasive method that can help clini-
cians identify appropriate CRC screenings for individuals (e.g., colonoscopy/sigmoidoscopy
for high risk and stool tests for low risk) is desired [34,35].
In this work, we use an artificial neural network (ANN) to score an individual’s risk of CRC
as a means of assisting screening recommendations. Previously, the ANN has been used to
assist diagnosis from expertly-collected data (e.g., distinguishing between sporadic colon ade-
nomas and cancers vs. inflammatory bowel disease-related dysplasia or cancer) from high-
dimensional genetic datasets [14]. This application is limited to use by professionals. In con-
trast, a comparatively-large portion of the population can self-report their habits of smoking,
exercise, their hypertension, diabetes, emphysema, and other personal-health data. Our ANN
is trained with this latter data specifically because of the ease of self-reporting per TRIPOD
guidelines [36]. This makes it inappropriate for use as a diagnostic tool, but able to be mass-
implemented. Members of the general public can make personal decisions to be screened [22]
based on their score of CRC risk, and clinicians can use this same score to assist their screening
Materials and methods
The data to train and validate the ANN was the 1997–2016 responses to the NHIS sample
adult questionnaire from the Centers for Disease Control and Prevention (CDC) [19]. About
20% of respondents were discarded in our study due to missing entries in the NHIS question-
naire. The NHIS data inquired about both colon and rectal cancer. Therefore, we are counting
anyone that had colon cancer, rectal cancer, or both colon and rectal cancer as a single inci-
dence of colorectal cancer. Subjects who were diagnosed with CRC more than 4 years prior to
the time of survey were discarded from the dataset and thus not used. While the NHIS dataset
records the age at which the respondent was professionally diagnosed with CRC (if at all), the
dataset does not record the time at which diagnoses of other predictors (e.g., diabetes) was
given. Therefore, to increase the probability that the predictors arose prior to the cancer we
only include those that were recently diagnosed with cancer (within four years prior to taking
the NHIS).
For the default model, 525,394 and 58,376 subjects were used to train and test the ANN
respectively, among whom 1,269 and 140 respondents were told by a doctor or other health
professional that they had CRC recently; that is, within 4 years of their taking the NHIS. (This
is referred to as “recently and professionally diagnosed with CRC” throughout the manuscript)
recently and professionally diagnosed with CRC. Variants of this model whose resulting ROCs
Scoring colorectal cancer risk based on personal health data
PLOS ONE | August 22, 2019 3 / 18
are studied in Fig 2 significantly change the number of subjects in each population. Each popu-
lation is given in Table 1.
From the NHIS data we obtained the factors appearing in Table 2. Heart conditions are
pooled into 1 factor, and training is done with and without data on hypertension and CRC
family history, leaving 12 to 14 factors. These factors were selected because they correlate
strongly with CRC incidence and also have either “ever” as their time of incidence (see Discus-
sion), are “permanent” (e.g., age, ethnicity), or are “per week” (vigorous exercise) frequency.
Fig 2. ROC curves of the ANN for ten-fold cross-testing dataset (TRIPOD 2a). The ANN trained with the factors
marked “default model” in Table 2 with (blue line) and without hypertension (purple line). The ANN was also trained
on a reduced dataset that included family history with (green line) and without (red line) hypertension. Error bars
denote the standard deviation of the TPR and FPR across ten folds of stratified cross-testing (TRIPOD level 2a).
Table 1. Number of NHIS respondents in final dataset when certain factors are chosen.
Ever screened? † Family history† Age (years) Hyper-
Training Training & CRC Testing Testing& CRC
Unused Used 18–85 Used 105,950 245 11,772 27
Unused Used 18–85 Unused 105,760 245 11,723 27
Unused Unused 18–85 Unused 525,394 1,269 58,376 140
Unused Unused 18–49 Used 298,085 162 33,120 18
Unused Unused 50–75 Used 227,310 1,107 25,255 122
Used Unused 18–85 Used 10,261 72 217,774 446
Used Used 18–85 Used 66,938 300 161,098 218
Used Used 18–85 Unused 9,002 59 219,176 459
Used Unused 18–85 Unused 13,755 71 212,995 443
† Data appearing in NHIS years 2000, 2005, 2010, and 2015 only when a set of supplementary questions were asked.
Data in the default model.
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The relative importance of these factors (in terms of Pearson correlation [37] with CRC) are
presented in Table 2.
One of the 14 factors we would like to use is hypertension, but the approval [38] of bevaci-
zumab as a treatment for CRC in 2004 can result in many people having hypertension because
of the treatment of CRC instead of being a risk factor for CRC itself. Therefore, we determined
the correlation of hypertension and CRC prior to 2004 (3.16×10
) and after 2004 (2.86×10
This is an unexpected post-approval [3,38] decrease of 3×10
. Thus, we decided bevacizu-
mab-induced hypertension was unimportant and we used hypertension in all models unless
indicated otherwise.
Raw data in the NHIS dataset was mapped to the interval [0,1] to be input to the ANN in
two ways depending on whether the data is categorical or ordinal. Referring to Table 2, the fac-
tors of having hypertension, ulcers, a stroke, and emphysema are binary variables and natu-
rally to map to 0 for “no”, 1 for “yes”. Diabetic status can have one of three discrete values: not
diabetic, pre-diabetic/borderline, and diabetic. These were mapped to 0, 0.5, and 1, respec-
tively. The age factor is continuous and is the age at the time of responding to the NHIS if the
respondent never had any cancer or the age at which they were recently and professionally
diagnosed with CRC otherwise. The factors of weekly frequency of vigorous exercise and BMI
are also continuous. The NHIS defines vigorous exercise as lasting 10 minutes or more and
resulting in one or more of: heavy sweating, breathing, or elevated heart rate. All such continu-
ous factors are unitized to the interval [0,1] using the replacement x)xminx
maxxminxfor factor x.
The factor sex is 0 for women and 1 for men. The variable of Hispanic ethnicity was given a
value of 0 for a response of “Not Hispanic/Spanish origin” and 1 otherwise. The variable of
race was set to 1 for responses of “Black/African American only”, “American Indian only”,
Table 2. All factors in the NHIS datasets used to train the ANN in scoring CRC risk, in descending order of correlation magnitude.
Name of Factor Correlation with Recent
CRC, ×10
Type of
# of Unique Values of
Time of Incidence, Frequency,
or Duration
Current or Cancer Age† +4.907 Continuous 68 Permanent
Hypertension† +3.045 Ordinal 2 Ever
Number of first-degree relatives with CRC (NHIS years 2000,
2005, 2010, and 2015 only)
+2.906 Ordinal 4 Permanent
Coronary heart disease +2.349 Ordinal 2 Ever
Pooled heart conditions† +2.063 Ordinal 2 Ever
Myocardial infarction +2.060 Ordinal 2 Ever
Diabetes (non-gestational) † +2.056 Ordinal 3 Ever
Heart condition/disease +1.972 Ordinal 2 Ever
Vigorous exercise frequency† -1.971 Continuous 33 Per week
Angina pectoris +1.769 Ordinal 2 Ever
Ulcer (stomach, duodenal, peptic)† +1.540 Ordinal 2 Ever
Hispanic ethnicity† -1.269 Categorical 2 Permanent
Stroke† +1.218 Ordinal 2 Ever
Emphysema† +1.220 Ordinal 2 Ever
American Indian, African American, other, or multiple race
-0.494 Categorical 2 Permanent
Sex (male) † -0.350 Categorical 2 Permanent
Body-mass index† +0.234 Continuous 4223 Current
Smoking frequency† +0.0461 Ordinal 4 Current
† Denotes factors that are part of the model referred to as “default” throughout this paper.
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“Other race”, or “Multiple race” and 0 otherwise (avoiding one-hot encoding to reduce overfit-
ting). The smoking status had a value of 1 for an everyday smoker, 0.66 for a some-day smoker,
0.33 for a former smoker, and 0 for a “never smoker”. The NHIS defines a “never smoker” as
one who has smoked 100 cigarettes or less over their entire life, and a “former smoker” as a
smoker who has quit at least 6 months. The variable of family history is the number of first-
degree relatives with CRC but is capped at 3, which is then mapped to values of 0, 1/3, 2/3, 1.
Finally, any answer of “yes” to coronary heart disease, myocardial infarction, heart disease,
and angina contributes 0.25 to the “Pooled heart conditions” field. These mapped values
(rather than the raw data) are used in Table 2 for the correlation calculation.
An artificial neural network (ANN)
An artificial neural network is a network of neurons, with each neuron being equivalent to a
logistic regression. A neuron’s inputs, the outputs of the preceding layer’s neurons, are com-
bined in a weighted sum with a bias term. This linear function is fed into a sigmoidal (“activa-
tion”) function to produce the neuron’s output. The input layer consists of the model’s input
data (the NHIS data). The output layer returns model predictions to the user. In this work, our
ANN has only one neuron in the output layer, representing an individual’s risk score for CRC.
Layers between the input and output layer are known as hidden layers. Therefore, an ANN is
essentially a statistical regression that is nonlinear with respect to the model parameters. Note
that with zero hidden layers the ANN is equivalent to logistic regression due to the logistic acti-
vation function.
Using weights W
and biases B
, the ANN forward-propagates from input
data Xto a risk score
Yby the following three compositions (indicated by parentheses) of the
logistic activation function σ=σ(z) having argument z(e.g., in the first logistic function, z=
Y¼ ½risk score ¼ sðB3þW3sðB2þW2sðB1þW1XÞÞÞ;sðzÞ  1=½ezþ1;ð1Þ
We used an in-house MATLAB code to minimize fitting error of (or “train”) our four-layered
ANN. Fig 1 shows an example of a four-layer ANN. Our ANN had 12 to 14 inputs, and each
hidden layer had the same number of neurons as the input layer. The cross-entropy loss func-
tion [37] comparing the model’s predictions, Yifor the i
NHIS-respondent’s CRC, with the
actual CRC status, Y
(0 for never-cancer and 1 for CRC), for Nrespondents is,
½loss ¼ 1
Yiþ ð1YiÞlnð1
YiÞÞ ð2Þ
Backpropagation minimizes the fitting error numerically. It involves the chain rule derivative
of Eq (1) with respect to weights W
and biases B
in iterative gradient decent
with the Adam learning rate. Because it numerically minimizes the fitting error Eq (2), back-
propagation plays the role of setting slope/intercept-derivatives of the sum-of-squares error
equal to zero and solving for slope and intercept in one-variable linear regression.
During training we used ten-fold cross-testing to test our model, while an additional test set
(NHIS year 2017 dataset) was held out for developing the stratification scheme after the ANN
was trained. Because there is zero intersection [37] between the training and testing datasets,
our model has TRIPOD levels [36] of 2a and 2b. The training was done with the standard
backpropagation algorithm with the “Adam” learning rate. Rather than selecting a decision
boundary to determine what output is considered a positive or negative cancer prediction, we
just use the raw output of the ANN of Fig 1 and refer to it, loosely, as a person’s risk of having
colorectal cancer.
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Model evaluation
The output of the ANN is a number from 0 to 1 which we treat as a risk-score. Using this out-
put, one can decide on a score-threshold above which the result is considered a positive for
CRC and otherwise is a negative result. To evaluate the performance of our ANN, we paramet-
rically plotted the sensitivity and specificity as a function of the risk-score threshold to produce
an ROC plot [39,40]. We created an analogous plot with the positive predictive value (PPV)
and negative predictive value (NPV).
Stratified cross-testing [41] with ten random folds (having zero intersection [37] with the
training dataset) was used to attain a TRIPOD level [36] of 2a. Two sources give statistical vari-
ance [37] in the concordance (C) statistic: the first is the number of cancer-cases within the
dataset (the population variance) and the second is the number of folds of stratified cross-test-
ing. Hanley and McNeil model the population variance as due to the risk-score being distrib-
uted normally (or as a Gaussian). The variance σ
is across n
stratified cross-validations. The
population-variance σ
and C-statistic A
is assumed to have a Gaussian distribution and is
thus estimated (with maximum likelihood) within a α×100% confidence interval for a popula-
tion of Ddiseased subjects and Hhealthy subjects by [40],
HD Aið1AiÞ þ ðD1ÞAi
 þ ðH1Þ2Ai
The total variance σ, assuming Gaussian distribution of population-variance [37] and a num-
ber n
= 10 of folds of cross-testing, is s2¼Xnxv
2=nxv, the average of the vari-
ances. Due to the tradeoff of having high variance [37] in the limits n
one-out” stratified cross-testing, where the testing set is just a single data point and thus D!
{0,1} = 1H) and high bias [37] in the limit n
= 2 (“split-sample” stratified cross-testing), the
appropriate number of stratified cross-validations must be determined empirically. We thus
decided upon ten-fold stratified cross-testing (n
= 10).
Stratifying by risk-score
To demonstrate the potential application of our ANN model in the clinic, we stratified 2017
NHIS respondents [19] into risk-categories. Based on the calculated CRC risk-score from our
model, we renormalized and selected a low/medium risk boundary and a medium/high risk
boundary to divide the calculated CRC risk into 3 categories. The two boundaries were chosen
such that no more than 1% of 1997–2016 NHIS respondents with cancer are categorized as
low risk, and no more than 1% of 1997–2016 NHIS respondents without cancer are classified
as high risk.
Model performance
In Fig 2, the receiver operating characteristic curves (ROC) [39,40] are plotted for the ANN
trained with data from Table 2. Cross-testing with ten folds is used to emphasize the generaliz-
ability of the training. The sensitivity, specificity, and concordance [18,40] of only the testing
across ten random folds (TRIPOD 2a) is reported in Fig 2. The crossed error bars in each ROC
are the standard deviation across the ten folds of cross-testing. Inclusion of family history data
requires removing a large number of respondents for whom this information is missing, yet
gives a performance comparable to the case where all NHIS years are included. It can be seen
that deviation from the mean performance of the default model is within 2 standard deviations
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PLOS ONE | August 22, 2019 7 / 18
in the error bars of Fig 2, showing insensitivity to addition or removal of the factors most
strongly correlating with being recently and professionally diagnosed with CRC.
At the risk-cutoff value where their sum is maximized, the sensitivity is of 0.57 ±0.03 and
the specificity is of 0.89 ±0.02 (mean value ±the standard deviation across the ten folds of
cross-testing). Uncertainty is larger in the sensitivity compared to the specificity because of the
low prevalence of CRC within the dataset. The uncertainty in the concordance has a contribu-
tion due to the population [39,40] and a contribution due to the standard deviation across
folds of stratified cross-testing [41]. The latter contribution is normally distributed [37] as a
result of random shuffling of the data before partitioning into folds of testing.
Cross-testing between the screened and unscreened
In Fig 3 we repeat the analysis of Fig 2, but only use respondents for whom family history data
was available. This eliminates any advantage the models without family history gain from hav-
ing a larger sample size. In addition, we show the performance resulting from cross-testing
between the group of those who were ever screened by colonoscopy/sigmoidoscopy and the
group formed by the remaining population. The green ROC (hypertension and family history)
with an AUC of 0.84 outperforms the blue ROC (hypertension but no family history) with an
AUC of 0.68. Similarly, the red ROC (no hypertension with family history) has an AUC of
0.75, better than the 0.58 AUC for the purple ROC (no hypertension no family history). Due
to the strong correlation of CRC family history with screening history (see Table 2), inclusion
of family history data sharply improves performance by greater than just a standard deviation
in testing upon a population not screened by colorectal exam after training on the examined
population, as reported in Fig 3. Without family history data, performance worsens by about
one standard deviation of true positive rate.
Fig 3. Cross-testing ROC curves of ANN for data non-randomly split between screened and non-screened NHIS
respondents (TRIPOD 2b). Using the reduced dataset the ANN was cross-tested between the group of survey
respondents (NHIS years 2000, 2005, 2010, and 2015 only) screened for CRC by colonoscopy/sigmoidoscopy and the
remaining year group.
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Performance in age groups
In Fig 4, ROC plots are given for an ANN trained and tested upon only ages 18–49, ages 50–
75, and all ages for tenfold random cross-testing dataset (TRIPOD 2a). These age groups were
formed because in ages 50–75 and ages 18–49, USPSTF screening guidelines [22] have respec-
tive sensitivity and specificity each of 100% (TRIPOD level 4) in flagging and not flagging a
person for screening. There is good [18] mean discrimination in ages 18–49, in which there
has been a recent rise in colorectal cancer incidence [11]. In contrast, in ages 50–75 the mean
discrimination is only acceptable [18]. The mean is across ten folds of stratified cross-testing
[41]. Due to the accompanying standard deviation across folds of cross-testing and the popula-
tion-variance [40], the performance in ages 18–49 extends down into being merely acceptable,
and the performance in ages 50–75 extends down into being indiscriminate [18]. Clearly, per-
formance in cross-testing within ages 18–49 is better than the performance in cross-testing
within ages 50–75.
Predictive value of the ANN
In Fig 5, the positive predictive value and false omission rate is reported for a wide variety of
risk-cutoffs. The positive predictive value of the trained ANN is much lower than its negative
predictive value at almost all values of the risk-cutoff, meaning that a negative call by the ANN
is far more meaningful than a positive call [37]. Due to the NHIS dataset being a cross-sec-
tional study with no follow-up, it remains possible that the false positives that drive the PPV
down to such a low value are those who are of high risk and have CRC that has not yet been
detected (whom it is highly desirable to screen). Barring this possibility, the ANN is better
suited for making screening recommendations than functioning as a diagnostic tool, and thus
is next demonstrated to calculate risk.
Fig 4. Cross-testing ROC curves of ANN for age groups formed by USPSTF screening guidelines. The ANN is
trained by and tested upon 3 datasets: ages 18–49, ages 50–75, and all ages for the full dataset. Error bars denote the
standard deviation of the TPR and FPR across ten-fold stratified cross-testing (TRIPOD level 2a).
Scoring colorectal cancer risk based on personal health data
PLOS ONE | August 22, 2019 9 / 18
3-category risk stratification
To test its potential application in stratifying colorectal cancer risk, we ran the developed ANN
with the 2017 NHIS dataset [19]. As shown in Fig 6, three categories of risk (green: low risk;
yellow: medium risk; red: high risk) are stratified. The solid-lined cumulative distributions are
the respective cancer/non-cancer 2017 populations correctly classified as high/low risk. The
Fig 5. Diagnostic performance of the ANN for the random testing dataset (TRIPOD level 2a). Positive predictive
value PPV and false omission rate were parametrically plotted in analogy to Fig 2.
Fig 6. Risk stratification into three categories. The 2017 NHIS respondents are stratified by the ANN into three
categories for CRC risk: green (low risk), yellow (medium risk), and red (high risk).
Scoring colorectal cancer risk based on personal health data
PLOS ONE | August 22, 2019 10 / 18
dashed-line cumulative distributions are the respective cancer/non-cancer 2017 populations
misclassified as low/high risk. The low/medium and medium/high risk boundaries are defined
by requiring a 1% misclassification rate for the 1997–2016 NHIS respondents. The CRC and
never-cancer 2017 NHIS respondents are misclassified at respective rates of 5% and 6%.
A summary of the results of stratifying the populations into three risk categories is given in
Table 3. Our ANN classifies 8% of the cancerous population as high score and 87% of the same
as medium risk with a misclassification rate of 5%. For the non-cancerous population, our
ANN classifies 12% of the non-cancer population as low score and 82% of the same as medium
risk with a misclassification rate of 6%. In comparison, the USPSTF guidelines misclassify 35%
of the cancerous population as low risk and 53% of the non-cancerous population as high risk
at TRIPOD level 4.
Due to the low value of its positive calls, the ANN will not replace current screening methods
for CRC. This can be seen by the comparison in Table 4 of the PPVs among conventional
screening methods with that of the trained ANN found in Fig 5. The gold standards of colo-
noscopy and sigmoidoscopy are the only tests with PPV close to 1, but these tests are invasive
and sometimes injurious [22]. Their lower-PPV counterparts, FIT, FOBT, and SEPT9, have
varied test accuracy depending on the CRC stage [42] (a feature shared by colonoscopy and
sigmoidoscopy, albeit to a lesser extent). Different levels of risk [43] as in Fig 6 could be
assigned different screening methods, depending on the judgment of the clinician and the
availability of resources (e.g., in some countries, the FIT is the only screening test available).
An example of such a scheme for concreteness: a clinician might preferentially give colonosco-
pies (most expensive) to those of high risk, SEPT9 and/or FOBT tests (moderate expense) to
those of medium risk, and FIT (least expensive) to those of low risk (see Table 4) [44,45].
Given the low cost, ease of mass implementation, and low invasiveness of the trained ANN, it
emerges as highly attractive for stratifying risk of CRC, despite its inability to perform
There are several aspects of our chosen methods that minimize the effects of potential
sources of bias in the calculated risk. While the ANN is a powerful statistical tool [46], the
ANN is only as good as the data used to train it, so a discussion of these biases is called for.
First, the outcome variable is defined as those NHIS respondents [19] recently and profes-
sionally diagnosed with CRC, and not necessarily those cases of CRC diagnosed by the gold
predictive standard of colonoscopy (see Table 4). Unfortunately, NHIS years 2000, 2005, 2010,
and 2015 only contain data on whether the respondents have ever been screened by sigmoidos-
copy, colonoscopy, or proctoscopy. In lieu of such screening data for every year, Fig 3 reports
testing-performance of a model trained on the screened population and tested on the remain-
ing population. Performance without family history is within a standard deviation of true
Table 3. Comparison of ANN risk-scoring with USPSTF screening guidelines on 2017 NHIS dataset for 3-category risk-score stratification.
# Respondents # Low Score % Low Score # Medium Score % Medium Score # High Score % High Score
CRC (2017) 60 3 5% 52 87% 5 8%
Never-cancer (2017) 25,457 2,932 12% 20,998 82% 1,527 6%
USPSTF Guidelines
CRC (2017) 60 21 35% n/a n/a 39 65%
Never-cancer (2017) 25,457 11,845 47% n/a n/a 13,612 53%
Scoring colorectal cancer risk based on personal health data
PLOS ONE | August 22, 2019 11 / 18
positive rate poorer, and is significantly greater with training by family history (which corre-
lates extremely strongly with having been screened). The decrease in performance without
family history data is attributable to the biases and confounders we discuss.
Another concern is that the predictor variables shown in Table 2 are marked as “ever” hav-
ing occurred, while the age at which the NHIS respondent was professionally diagnosed with
CRC is recorded. This is the purpose of the 4 years cutoff beyond which an instance of CRC is
regarded as too long ago from the date of taking the NHIS, and is discarded, thus decreasing
(if not eliminating) the probability that the predictors came before the CRC diagnosis. Previ-
ous work scoring risk of lung and skin cancer [43,47] have found their ANN insensitive to this
The correlation of a given factor with CRC incidence (see Table 2) is a necessary but insuffi-
cient condition for that factor to be definitely causal of CRC. This is why the effect of training
with and without hypertension data in the training of the ANN is indicated in Fig 2. Patients
taking bevacizumab [3] for CRC often develop high blood pressure [4,5], and if this was largely
responsible for the high correlation found in Table 2 it would be inappropriate to train the
ANN with this. As it turns out, bevacizumab was FDA-approved [38] as a second-line treat-
ment of metastatic CRC in 2004 and the NHIS dataset [19] of personal health data extends
from years 1997–2017. The correlation of hypertension with CRC in years 1997–2003 is
and in years 2004–2017 decreases (unexpectedly) to 2.97×10
. It is thus possible
that the predictor variables in Table 2 have their confounding with those NHIS respondents
Table 4. Comparison of ANN to conventional screening methods.
Screening method Sensitivity, Specificity and/
or PPV
Advantages Disadvantages
Artificial neural network (ANN) trained with NHIS data years 1997–
2016 tested on ten random splits
• Sensitivity ~ of 0.57 ±0.03
• Specificity ~ of 0.89 ±0.02
• PPV ~ of 0.0075 ±0.0003
• Better performance w/more
training data
• Privacy
• Inexpensive
• Stage-independent
• Can stratify risk
• Low PPV
• Assumes integrity of data
• Only correlation
• Cannot be used for screening
Guaiac or immunoassay fecal occult blood test (gFOBT or iFOBT) • Sensitivity ~ 0.9
• Specificity ~ 0.9
• PPV ~ 0.02
• No pre-test colon-cleansing
• Privacy
• Non-invasive
• Low PPV
• Pre-test diet
• False-positives
• Depends on CRC stage
• Moderately expensive
• Fecal immunochemical test (FIT)
• Fecal immunochemical DNA test (FIT-DNA)
(1) For FIT:
• Sensitivity ~ 0.1
• Specificity ~ 0.9
• PPV ~ 0.4
(2) For FIT-DNA:
• Sensitivity ~ 0.2
• Specificity ~ 0.9
• PPV ~ 0.5
• No pre-test colon-cleansing
• Privacy
• Inexpensive ($14)
• Non-invasive
• Adenoma insensitivity
• False-positives
• Low PPV
• Depends on CRC stage
Methylated SEPT9 gene test • Sensitivity ~ 0.6 at Stage I.
• Sensitivity ~ 0.9 at Stage
• No pre-test colon-cleansing
• Privacy
• Noninvasive
• Moderately expensive
• Depends on CRC stage
Flexible sigmoidoscopy • Sensitivity ~ 0.6
• Specificity ~ 0.7
• PPV ~ 0.8
• Able to perform biopsy/
• Less colon-cleansing
• No sedation
• Only rectum, lower-colon
• Dieting, bowel cleansing
• Invasive
• Expensive
Virtual colonoscopy • Sensitivity ~ 0.6
• Specificity ~ 0.7
• PPV ~ 0.8
• Noninvasive
• Sedation unneeded
• Better at identifying advanced
• Colon-cleansing
• Ionizing radiation
• Expensive (~$8000 in costs
and charges)
Scoring colorectal cancer risk based on personal health data
PLOS ONE | August 22, 2019 12 / 18
recently and professionally diagnosed with CRC controlled to some degree. This allows inclu-
sion of hypertension as a predictor, which is important due to the role of hypertension in CRC
recurrence and mortality [48,49].
Another source of bias lies in what age the NHIS respondent was recently and profes-
sionally diagnosed with CRC. There is a bias towards screening at that age interval because this
age interval was selected by the USPSTF [22], and this is seen on a histogram of CRC incidence
vs. age of diagnosis where it is observed that about 60% - 70% of CRC cases occur in ages 50–
75. Clearly, the diagnosis came at the time of screening, so the screening time could be far after
the time the CRC first nucleated. This may call for an augmentation of the NHIS survey with a
question about the extent of the CRC’s advancement when it was first detected by screening.
The last source of bias is due to discarding any NHIS respondent with one or more blank
entries as a result of answers of “refused”, “not ascertained”, or “don’t know” (see NHIS docu-
mentation [19]). It is speculated that responses of “not ascertained” and “don’t know” belong
to data missing completely at random, while the response of “refused” belongs to data missing
at random [50]. For instance, an answer of “refused” to the factor of a subject’s smoking habits
may mean smoking of illegal substances. If this is true, then our model would carry the bias
[37] of excluding (and thus less accurately scoring CRC risk in) the corresponding members of
the population. A similar bias results from the model excluding those having CRC more than 4
years from the year they answered the NHIS, which are also discarded. Future work would
draw upon imputation techniques allowing for systematic treatment of such missing entries,
thus avoiding the loss of an entire survey-respondent. This is especially critical in fields with
larger quantities of missing data, such as family history of CRC heavily relied upon by clini-
cians to make screening recommendations.
We now discuss the ANN architecture. An ANN with two hidden layers is selected due to
its being the smallest ANN that can learn low-degree polynomial functions [51]. This allows
the ANN to deal with noise. The ANN’s good performance in spite of the bias and confound-
ing discussed above could be attributable to it viewing these as noise. The cross-tested C-statis-
tic of logistic regression [18] (ROC not shown) is 0.60 ±0.03, suggesting the importance of
inter-factor coupling whose incorporation is made possible by a second hidden layer [21]. In
the ANN, factors are fed into any one trained neuron of the first hidden layer, linearly com-
bined by the weights, and composed with the sigmoid function. Each neuron of the first hid-
den layer thus takes on a value that is the pooled effect of all factors of the input-layer. Each
neuron of the second hidden layer receives these various pooled values, and thus is the layer
where the trained weights incorporate inter-factor couplings before being fed into the single
output-neuron (CRC risk score). If the second hidden layer is made of a single neuron, it
becomes equivalent to the output neuron and the calculated risk incorporates no inter-factor
coupling. This gives a C-statistic of ~0.5, which means the ANN is almost maximally indiscri-
minant [18]. We interpret this (as well as the insensitivity of the C-statistic to presence/absence
of data on hypertension and family history reported in Fig 2 and Fig 5) as indicative of how
much more important inter-factor coupling in one’s risk of CRC is compared to factors acting
in isolation (which the process of naïve one-by-one variable selection assumes). The insensitiv-
ity of the ANN to being trained or not trained with cancer-family-history data (NHIS years
2000, 2005, 2010, and 2015 only) shown in Fig 2 might also be attributable to inter-factor cou-
pling [21].
A model of CRC risk within the NHIS dataset [19] has been developed thusly. The model
shows predictive power in a general demographic in Fig 2. The resulting concordance is
0.80 ±0.05, and thus is competitive with that of Kaminski et al.[15] (which incorporates family
history of CRC as well as regular aspirin use [52]) and with that of the highest-performing
models (among 11) [15] in a recent review of MEDLINE, Scopus, and Cochrane Library
Scoring colorectal cancer risk based on personal health data
PLOS ONE | August 22, 2019 13 / 18
databases from January 1990 through March 2013 [17]. In Fig 3, because performance
improves by greater than one standard deviation of true positive rate on including family his-
tory data and worsens by one standard deviation of true positive rate, the worsening of perfor-
mance can be attributable to the sources of bias described above (taking colorectal exams to be
the gold standard of diagnosis as in Table 4). The trained ANN retains its predictive power
even within ages 18–49: in Fig 4, the standard deviation of true positive rate approximately
reaches the performance at all ages. This suggests promise in performance in the demographic
not flagged for screening by age. Although the ANN’s positive calls are almost meaningless
(see Fig 5), it is usable as a risk stratification tool (see Fig 6 and Table 3) to assist clinicians.
Future work will study generalizability. The present work omits a calibration plot, and just
report discrimination due to this being a development (rather than validation) study. Future
work will study training/testing with the NHIS data and testing/training upon a separate data-
set to determine the effect of disparate risks of CRC between datasets upon model perfor-
mance. Advanced techniques such as validation-based early stopping and/or dropout are also
to be used.
A future aim is to implement this system of risk scoring in a software-application (an
“app”) that a smartphone can run. The application will be along the lines of CT Gently [53], an
application previously developed. The user of the application will answer NHIS survey ques-
tions and immediately receive the scoring of their risk for CRC, in much the same manner as a
current website [54] hosting an algorithm underpinning the corresponding published [55]
model. Moreover, the application will simulate immediate adjustments in the user’s personal
health habits: for instance, the user will be able to drag a sliding-bar representing their smoking
habits from high to low and see their score of CRC-risk drop in response to quitting smoking.
Alternatively, the ANN could retrieve electronic medical records in a clinical setting and pro-
vide immediate risk-scoring during consultation.
A multi-parameterized artificial neural network was developed to score risk of colorectal can-
cer based solely on personal health data. The trained ANN has been robustly tested per TRI-
POD level 2a and level 2b protocols. The concordance of the ANN is comparable to that of
current methods of scoring CRC risk (including those using biomarkers). The ANN outper-
forms logistic regression, suggesting the importance of inter-factor coupling. The low positive
predictive value indicates unsuitability of the ANN to replace conventional screening methods.
Nevertheless, in comparison to USPSTF guidelines, the trained ANN can stratify individual’s
colorectal cancer risk more accurately for more effective screening and intervention. As the
ANN is built from self-reportable personal health data, it can be easily implemented on a
mobile platform for more widespread applications.
Research reported in this publication was supported by the National Institute of Biomedical
Imaging and Bioengineering of the National Institutes of Health under Award Number
R01EB022589. DAR was funded by R01EB022589 while writing the first working version of
the ANN, which BJN et al continued under R01EB022589 while DAR went on to be supported
with salary by Sun Nuclear Corporation. Sun Nuclear Corporation did not have any additional
role in the study design, data collection and analysis, decision to publish, or preparation of the
manuscript. The specific role of DAR is articulated in the ‘author contributions’ section. The
content is solely the responsibility of the authors and does not necessarily represent the official
views of the National Institutes of Health or of Sun Nuclear Corporation.
Scoring colorectal cancer risk based on personal health data
PLOS ONE | August 22, 2019 14 / 18
Author Contributions
Conceptualization: Gregory R. Hart, David A. Roffman, Xavier Llor, Issa Ali, Wazir Muham-
mad, Ying Liang, Jun Deng.
Formal analysis: Bradley J. Nartowt.
Funding acquisition: Jun Deng.
Investigation: Bradley J. Nartowt, Gregory R. Hart, Xavier Llor, Issa Ali, Wazir Muhammad,
Ying Liang.
Methodology: Gregory R. Hart, Wazir Muhammad, Ying Liang.
Project administration: Jun Deng.
Resources: Jun Deng.
Software: Bradley J. Nartowt, David A. Roffman.
Supervision: Jun Deng.
Validation: Bradley J. Nartowt.
Visualization: Bradley J. Nartowt.
Writing – original draft: Bradley J. Nartowt.
Writing – review & editing: Bradley J. Nartowt, Gregory R. Hart, David A. Roffman, Xavier
Llor, Issa Ali, Wazir Muhammad, Ying Liang, Jun Deng.
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... Nine studies provided rationale for their choice of candidate predictors (e.g., based on previous research) [60,61,63,[66][67][68][69][70][71] and one study forced a-priori predictors during model development [72] (Table 2). Fifty-six studies (90%) clearly reported their candidate predictors and a median of 16 candidate predictors were considered per study (IQR: 12 to 26, range: 4-33,788). ...
... Of the 14 development with validation (external) studies, two used geographical validation [49,90], three used temporal validation [63,71,103] and 9 used independent data that was geographically and temporally different from the development data to validate their models [58,61,69,75,80,84,86,93,95]. Seven studies (50%) reported differences and similarities in definitions between the development and validation data [58,61,69,71,75,84,90]. ...
... Of the 14 development with validation (external) studies, two used geographical validation [49,90], three used temporal validation [63,71,103] and 9 used independent data that was geographically and temporally different from the development data to validate their models [58,61,69,75,80,84,86,93,95]. Seven studies (50%) reported differences and similarities in definitions between the development and validation data [58,61,69,71,75,84,90]. ...
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Background Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology. Methods We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models. Results Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified ( n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% ( n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation ( n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available. Conclusions The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.
... Artificial intelligence-based algorithms have proven to be able to assess unstructured data and accurately estimate the probability of patients developing various diseases including cancer [41]. Agnostic AI models can refine risk-stratification definitions and impact decisions on cancer screening recommendations [44][45][46][47][48][49] with satisfactory accuracy. For example, an artificial neural network model for colorectal cancer risk stratification showed improved accuracy when compared with current screening guidelines, by reducing false positives (i.e., individuals misclassified as high risk) from 53 to 6% and false negatives (i.e., individuals misclassified as low risk) from 35 to 5% [45]. ...
... Agnostic AI models can refine risk-stratification definitions and impact decisions on cancer screening recommendations [44][45][46][47][48][49] with satisfactory accuracy. For example, an artificial neural network model for colorectal cancer risk stratification showed improved accuracy when compared with current screening guidelines, by reducing false positives (i.e., individuals misclassified as high risk) from 53 to 6% and false negatives (i.e., individuals misclassified as low risk) from 35 to 5% [45]. ...
... High-risk individuals not included in the current screening guidelines but who are still at high risk for cancer development would likely be identified and benefit from early assessment. For example, screening for patients with early-onset sporadic colorectal cancer is limited by traditional methods, but may potentially benefit from intensive risk-based screening recommendations [45]. ...
Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes – prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.
... For instance, machine learning methods that integrate clinical risk factors have been applied to breast cancer risk prediction and improve predictive accuracy from 60% to 90% [15]. In addition, deep learning with an artificial neural network based on personal health data has been shown to robustly stratify CRC risk in the large national database [16]. Thus, we hypothesize that machine learning can integrate readily available and complex factors from electronic health records (EHRs) to create a prediction model for CRC that applies to adults aged 35-50. ...
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Background and aimsThe incidence of colorectal cancer (CRC) is increasing in adults younger than 50, and early screening remains challenging due to cost and under-utilization. To identify individuals aged 35-50 years who may benefit from early screening, we developed a prediction model using machine learning and electronic health record (EHR)-derived factors.Methods We enrolled 3,116 adults aged 35-50 at average-risk for CRC and underwent colonoscopy between 2017-2020 at a single center. Prediction outcomes were (1) CRC and (2) CRC or high-risk polyps. We derived our predictors from EHRs (e.g., demographics, obesity, laboratory values, medications, and zip code-derived factors). We constructed four machine learning-based models using a training set (random sample of 70% of participants): regularized discriminant analysis, random forest, neural network, and gradient boosting decision tree. In the testing set (remaining 30% of participants), we measured predictive performance by comparing C-statistics to a reference model (logistic regression).ResultsThe study sample was 55.1% female, 32.8% non-white, and included 16 (0.05%) CRC cases and 478 (15.3%) cases of CRC or high-risk polyps. All machine learning models predicted CRC with higher discriminative ability compared to the reference model [e.g., C-statistics (95%CI); neural network: 0.75 (0.48-1.00) vs. reference: 0.43 (0.18-0.67); P = 0.07] Furthermore, all machine learning approaches, except for gradient boosting, predicted CRC or high-risk polyps significantly better than the reference model [e.g., C-statistics (95%CI); regularized discriminant analysis: 0.64 (0.59-0.69) vs. reference: 0.55 (0.50-0.59); P
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Purpose In this scoping review, we examined the international literature on risk-stratified bowel screening to develop recommendations for future research, practice and policy. Methods Six electronic databases were searched from inception to 18 October 2021: Medline, Embase, PsycINFO, CINAHL, Cochrane Database of Systematic Reviews and Cochrane Central Register of Controlled Trials. Forward and backwards citation searches were also undertaken. All relevant literature were included. Results After de-deduplication, 3,629 records remained. 3,416 were excluded at the title/abstract screening stage. A further 111 were excluded at full-text screening stage. In total, 102 unique studies were included. Results showed that risk-stratified bowel screening programmes can potentially improve diagnostic performance, but there is a lack of information on longer-term outcomes. Risk models do appear to show promise in refining existing risk stratification guidelines but most were not externally validated and less than half achieved good discriminatory power. Risk assessment tools in primary care have the potential for high levels of acceptability and uptake, and therefore, could form an important component of future risk-stratified bowel screening programmes, but sometimes the screening recommendations were not adhered to by the patient or healthcare provider. The review identified important knowledge gaps, most notably in the area of organisation of screening services due to few pilots, and what risk stratification might mean for inequalities. Conclusion We recommend that future research focuses on what organisational challenges risk-stratified bowel screening may face and a consideration of inequalities in any changes to organised bowel screening programmes.
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Colorectal cancer is one of the most common types of cancer, and it can have a high mortality rate if left untreated or undiagnosed. The fact that CRC becomes symptomatic at advanced stages highlights the importance of early screening. The reference screening method for CRC is colonoscopy, an invasive, time-consuming procedure that requires sedation or anesthesia and is recommended from a certain age and above. The aim of this study was to build a machine learning classifier that can distinguish cancer from non-cancer samples. For this, circulating tumor cells were enumerated using flow cytometry. Their numbers were used as a training set for building an optimized SVM classifier that was subsequently used on a blind set. The SVM classifier’s accuracy on the blind samples was found to be 90.0%, sensitivity was 80.0%, specificity was 100.0%, precision was 100.0% and AUC was 0.98. Finally, in order to test the generalizability of our method, we also compared the performances of different classifiers developed by various machine learning models, using over-sampling datasets generated by the SMOTE algorithm. The results showed that SVM achieved the best performances according to the validation accuracy metric. Overall, our results demonstrate that CTCs enumerated by flow cytometry can provide significant information, which can be used in machine learning algorithms to successfully discriminate between healthy and colorectal cancer patients. The clinical significance of this method could be the development of a simple, fast, non-invasive cancer screening tool based on blood CTC enumeration by flow cytometry and machine learning algorithms.
Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public. © The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Rectal magnetic resonance imaging (MRI) is the preferred method for the diagnosis of rectal cancer as recommended by the guidelines. Rectal MRI can accurately evaluate the tumor location, tumor stage, invasion depth, extramural vascular invasion, and circumferential resection margin. We summarize the progress of research on the use of artificial intelligence (AI) in rectal cancer in recent years. AI, represented by machine learning, is being increasingly used in the medical field. The application of AI models based on high-resolution MRI in rectal cancer has been increasingly reported. In addition to staging the diagnosis and localizing radiotherapy, an increasing number of studies have reported that AI models based on high-resolution MRI can be used to predict the response to chemotherapy and prognosis of patients.
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Background & Objective: Colorectal cancer (CRC) is one of the most prevalent malignancies in the world. The early detection of CRC is not only a simple process but also is the key to treatment. Data mining algorithms could be potentially useful in cancer prognosis, diagnosis, and treatment. Therefore, the main focus of this study is to measure the performance of some data mining classifier algorithms in predicting CRC and providing an early warning to the high-risk groups. Materials & Methods: This study was performed on 468 subjects, including 194 CRC patients and 274 non-CRC cases. We used the CRC dataset from Imam Hospital, Sari, Iran. The Chi-square feature selection method was utilized to analyze the risk factors. Next, four popular data mining algorithms were compared in terms of their performance in predicting CRC, and, finally, the best algorithm was identified. Results: The best outcome was obtained by J-48 with F-measure=0.826, receiver operating characteristic (ROC)=0.881, precision=0.826, and sensitivity =0.827. Bayesian net was the second-best performer (F-Measure=0.718, ROC=0.784, precision=0.719, and sensitivity=0.722) followed by random forest (F-Measure=0.705, ROC=0.758, precision=0.719, and sensitivity=0.712). The multilayer perceptron technique had the worst performance (F-Measure=0.702, ROC=0.76, precision=0.701, and sensitivity=0.703). Conclusion: According to the results of this study, J-48 could provide better insights than other proposed prediction models for clinical applications. © 2021, Zanjan University of Medical Sciences and Health Services. All rights reserved.
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The objective of this study is to train and validate a multi-parameterized artificial neural network (ANN) based on personal health information to predict lung cancer risk with high sensitivity and specificity. The 1997-2015 National Health Interview Survey adult data was used to train and validate our ANN, with inputs: gender, age, BMI, diabetes, smoking status, emphysema, asthma, race, Hispanic ethnicity, hypertension, heart diseases, vigorous exercise habits, and history of stroke. We identified 648 cancer and 488,418 non-cancer cases. For the training set the sensitivity was 79.8% (95% CI, 75.9%-83.6%), specificity was 79.9% (79.8%-80.1%), and AUC was 0.86 (0.85-0.88). For the validation set sensitivity was 75.3% (68.9%-81.6%), specificity was 80.6% (80.3%-80.8%), and AUC was 0.86 (0.84-0.89). Our results indicate that the use of an ANN based on personal health information gives high specificity and modest sensitivity for lung cancer detection, offering a cost-effective and non-invasive clinical tool for risk stratification.
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Ultraviolet radiation (UVR) exposure and family history are major associated risk factors for the development of non-melanoma skin cancer (NMSC). The objective of this study was to develop and validate a multi-parameterized artificial neural network based on available personal health information for early detection of NMSC with high sensitivity and specificity, even in the absence of known UVR exposure and family history. The 1997-2015 NHIS adult survey data used to train and validate our neural network (NN) comprised of 2,056 NMSC and 460,574 non-cancer cases. We extracted 13 parameters for our NN: gender, age, BMI, diabetic status, smoking status, emphysema, asthma, race, Hispanic ethnicity, hypertension, heart diseases, vigorous exercise habits, and history of stroke. This study yielded an area under the ROC curve of 0.81 and 0.81 for training and validation, respectively. Our results (training sensitivity 88.5% and specificity 62.2%, validation sensitivity 86.2% and specificity 62.7%) were comparable to a previous study of basal and squamous cell carcinoma prediction that also included UVR exposure and family history information. These results indicate that our NN is robust enough to make predictions, suggesting that we have identified novel associations and potential predictive parameters of NMSC.
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Zika virus (ZIKV) has recently caused a pandemic disease, and many cases of ZIKV infection in pregnant women resulted in abortion, stillbirth, deaths and congenital defects including microcephaly, which now has been proposed as ZIKV congenital syndrome. This study aimed to investigate the in situ immune response profile and mechanisms of neuronal cell damage in fatal Zika microcephaly cases. Brain tissue samples were collected from 15 cases, including 10 microcephalic ZIKV-positive neonates with fatal outcome and five neonatal control flavivirus-negative neonates that died due to other causes, but with preserved central nervous system (CNS) architecture. In microcephaly cases, the histopathological features of the tissue samples were characterized in three CNS areas (meninges, perivascular space, and parenchyma). The changes found were mainly calcification, necrosis, neuronophagy, gliosis, microglial nodules, and inflammatory infiltration of mononuclear cells. The in situ immune response against ZIKV in the CNS of newborns is complex. Despite the predominant expression of Th2 cytokines, other cytokines such as Th1, Th17, Treg, Th9, and Th22 are involved to a lesser extent, but are still likely to participate in the immunopathogenic mechanisms of neural disease in fatal cases of microcephaly caused by ZIKV.
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Aim: To summarize and compare worldwide colorectal cancer (CRC) screening recommendations in order to identify similarities and disparities. Methods: A systematic literature search was performed using MEDLINE, EMBASE, Scopus, CENTRAL and ISI Web of knowledge identifying all average-risk CRC screening guideline publications within the last ten years and/or position statements published in the last 2 years. In addition, a hand-search of the webpages of National Gastroenterology Society websites, the National Guideline Clearinghouse, the BMJ Clinical Evidence website, Google and Google Scholar was performed. Results: Fifteen guidelines were identified. Six guidelines were published in North America, four in Europe, four in Asia and one from the World Gastroenterology Organization. The majority of guidelines recommend screening average-risk individuals between ages 50 and 75 using colonoscopy (every 10 years), or flexible sigmoidoscopy (FS, every 5 years) or fecal occult blood test (FOBT, mainly the Fecal Immunochemical Test, annually or biennially). Disparities throughout the different guidelines are found relating to the use of colonoscopy, rank order between test, screening intervals and optimal age ranges for screening. Conclusion: Average risk individuals between 50 and 75 years should undergo CRC screening. Recommendations for optimal surveillance intervals, preferred tests/test cascade as well as the optimal timing when to start and stop screening differ regionally and should be considered for clinical decision making. Furthermore, local resource availability and patient preferences are important to increase CRC screening uptake, as any screening is better than none.
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Background: The relationship between dietary and lifestyle risk factors and long-term mortality from colorectal cancer is poorly understood. Several factors, such as obesity, intakes of red meat, and use of aspirin, have been reported to be associated with risk of colorectal cancer mortality, though these findings have not been replicated in all studies to date. Methods: In the Minnesota Colon Cancer Control Study, 46,551 participants 50-80 years old were randomly assigned to usual care (control) or annual or biennial screening by fecal occult blood testing. Colon cancer mortality was assessed after 30 years of follow-up. Dietary intake and lifestyle risk factors were assessed by questionnaire at baseline. Results: Age [hazard ratio (HR) 1.09; 95% CI 1.07, -1.11], male sex (HR 1.25; 95% CI 1.01, 1.57), and higher body mass index (BMI) (HR 1.03; 95% CI 1.00-1.05) increased the risk of CRC mortality, while undergoing screening for CRC was associated with a reduced risk of colorectal cancer mortality (HR 0.76; 95% CI 0.61-0.94 and 0.67; 95% CI 0.53-0.83 for biennial and annual screening, respectively). Intakes of grains, meats, proteins, coffee, alcohol, aspirin, fiber, fruits, and vegetables were not associated with colorectal cancer mortality. Conclusions: Our study confirms the relationship between BMI and long-term colorectal cancer mortality. Modulation of BMI may reduce risk of CRC mortality.
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Colorectal cancer (CRC) has become the third most common cancer in the world. Screening has been shown to be an effective way to identify early CRC and precancerous lesions, and to reduce its morbidity and mortality. Several types of noninvasive tests have been developed for CRC screening, including the fecal occult blood test (FOBT), the fecal immunochemical test (FIT), the fecal-based DNA test and the blood-based DNA test (the SEPT9 assay). FIT has replaced FOBT and become the major screening test due to high sensitivity, specificity and low costs. The fecal DNA test exhibited higher sensitivity than FIT but its current cost is high for a screening assay. The SEPT9 assay showed good compliance while its performance in screening needs further improvements. These tests exhibited distinct sensitivity and specificity in screening for CRC and adenoma. This article will focus on the performance of the current noninvasive in vitro diagnostic tests that have been used for CRC screening. The merits and drawbacks for these screening methods will also be compared regarding the techniques, usage and costs. We hope this review can provide suggestions for both the public and clinicians in choosing the appropriate method for CRC screening.
This review will comprise a general overview of colorectal cancer (CRC) screening. We will cover the impact of CRC, CRC risk factors, screening modalities, and guideline recommendations for screening in average-risk and high-risk individuals. Based on this data, we will summarize our approach to CRC screening.
Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data were collected by the National Center for Health Statistics. In 2017, 1,688,780 new cancer cases and 600,920 cancer deaths are projected to occur in the United States. For all sites combined, the cancer incidence rate is 20% higher in men than in women, while the cancer death rate is 40% higher. However, sex disparities vary by cancer type. For example, thyroid cancer incidence rates are 3-fold higher in women than in men (21 vs 7 per 100,000 population), despite equivalent death rates (0.5 per 100,000 population), largely reflecting sex differences in the "epidemic of diagnosis." Over the past decade of available data, the overall cancer incidence rate (2004-2013) was stable in women and declined by approximately 2% annually in men, while the cancer death rate (2005-2014) declined by about 1.5% annually in both men and women. From 1991 to 2014, the overall cancer death rate dropped 25%, translating to approximately 2,143,200 fewer cancer deaths than would have been expected if death rates had remained at their peak. Although the cancer death rate was 15% higher in blacks than in whites in 2014, increasing access to care as a result of the Patient Protection and Affordable Care Act may expedite the narrowing racial gap; from 2010 to 2015, the proportion of blacks who were uninsured halved, from 21% to 11%, as it did for Hispanics (31% to 16%). Gains in coverage for traditionally underserved Americans will facilitate the broader application of existing cancer control knowledge across every segment of the population. CA Cancer J Clin 2017. © 2017 American Cancer Society.