Risk Prediction Models For Colorectal Cancer Research Articles

Challenges With Colorectal Cancer Family History Assessment—Motivation to Translate Polygenic Risk Scores Into Practice

Challenges With Colorectal Cancer Family History Assessment—Motivation to Translate Polygenic Risk Scores Into Practice

Progress in construction and verification of colorectal cancer risk prediction models: a systematic review

Objective: To systematically review available risk prediction models evidence on construction and verification of colorectal cancer risk prediction models. Methods: "Colorectal neoplasms", "risk assessment", "colorectal cancer", "colorectal tumor", "colon cancer", "colon tumor", "rectal cancer", "rectal tumor", "anal cancer", "anal tumor", "risk prediction", "malignancy", "carcinogenesis", "model" were used as search keywords. Journal papers and grey literature were searched from Chinese electronic databases (CNKI and Wanfang) and English electronic databases (PubMed and Embase) from their inception to 30 Apr 2018. The language of literature was restricted to Chinese and English. The inclusion criteria were human-oriented researches with complete information for model construction,verification and evaluation. The exclusion criteria were informal publications such as conference abstracts, Chinese disertation papers, and non-primary research materials such as reviews,letters,and news reports. Descriptive characteristics,targeted population, study design, model construction method and prediction results were extracted. A total of 36 papers involving 27 models were included. The population characteristics of all included studies,the type of research, the method of model construction and the prediction results of the model were analyzed. Results: As for model construction,there were 13 European and American population based model studies,14 Asian population based model studies,including 7 Chinese mainland based model studies. According to the factors selected into the model, these models can be divided into traditional epidemiological models (17 models), clinical index combined models (4 models),and genetic susceptibility index combined models (6 models). As for model verification,only 9 models were cross-verified in the internal population after model construction, and the extrapolation of model prediction effect was not effectively evaluated; 17 models were verified in an external population; there was only one model verified in two external populations in terms of risk prediction effect; the area under the curve of 27 models was 0.56-0.85. Conclusion: The risk prediction model of colorectal cancer is in the development stage. The external evaluation of model prediction effect is less and the prediction ability is not good, and the existing models have limited exploration of clinical indicators.

Multiple risk factors prediction models for high risk population of colorectal cancer

Colorectal cancer is caused by the interaction of genetic and environment factors. Domestic and foreign scholars have attempted to develop several colorectal cancer risk prediction models, in order to identity risk factors, to screen for high risk population and evaluate the risk of developing colorectal cancer, so as to provide personalized screening protocols for individuals with different risk, and eventually reduce the incidence and mortality rate of colorectal cancer. Currently, the common colorectal cancer risk prediction models were mainly developed based on case-control study and cohort study. Models developed in European and American regions and Asia (excluding China) only include common risk factors, while Chinese models also include hereditary factors on the bases of common risk factors. However, the development and verification of each model are mainly based on local population, whether it can be applied for other population need to be determined. This article reviews the development, validation and evaluation of the risk prediction models, in order to provide a basis for developing more precise risk prediction models for colorectal cancer.

Predicting the risk for colorectal cancer with personal characteristics and fecal immunochemical test.

We aimed to predict colorectal cancer (CRC) based on the demographic features and clinical correlates of personal symptoms and signs from Tianjin community-based CRC screening data.A total of 891,199 residents who were aged 60 to 74 and were screened in 2012 were enrolled. The Lasso logistic regression model was used to identify the predictors for CRC. Predictive validity was assessed by the receiver operating characteristic (ROC) curve. Bootstrapping method was also performed to validate this prediction model.CRC was best predicted by a model that included age, sex, education level, occupations, diarrhea, constipation, colon mucosa and bleeding, gallbladder disease, a stressful life event, family history of CRC, and a positive fecal immunochemical test (FIT). The area under curve (AUC) for the questionnaire with a FIT was 84% (95% CI: 82%–86%), followed by 76% (95% CI: 74%–79%) for a FIT alone, and 73% (95% CI: 71%–76%) for the questionnaire alone. With 500 bootstrap replications, the estimated optimism (<0.005) shows good discrimination in validation of prediction model.A risk prediction model for CRC based on a series of symptoms and signs related to enteric diseases in combination with a FIT was developed from first round of screening. The results of the current study are useful for increasing the awareness of high-risk subjects and for individual-risk-guided invitations or strategies to achieve mass screening for CRC.

Prevalence of the population ‘at risk’ of developing colorectal cancer in Sri Lanka

Background: Colorectal cancer is the fourth leading cancer among men and sixth leading cancer among women in Sri Lanka. Objective: To estimate the prevalence of ‘being at risk’ of developing colorectal cancer among adults using a colorectal cancer risk prediction model developed and validated to the Sri Lankan setting. Methods: A community-based cross-sectional descriptive study was conducted among a representative sample of 811 adults aged 30 years and above in the districts of Colombo and Gampaha, selected using a multi-stage cluster sampling technique. The validated risk prediction model in the form of an interviewer-administered questionnaire was used to gather information. The prevalence of those ‘at risk’ of colorectal cancer was assessed based on a validated cut-off score, and those ‘at risk’ were further divided as ‘moderate’ and ‘high risk’ based on cut-off values agreed upon by the experts. Results: The age-adjusted prevalence of those ‘at risk’ was 12.5% (95% CI=12.3, 12.7). Age-adjusted prevalence of those at ‘moderate’ and ‘high’ risk were 11.8% (95% CI=11.6, 12.0) and 0.72% (95% CI=0.7, 0.8), respectively. Conclusions: The high prevalence of being ‘at risk’ of colorectal cancer indicates the necessity of screening for colorectal cancer as a programme in Sri Lanka. Considering the logistic difficulties in offering follow-up diagnostic colonoscopy examinations for all, the study recommends that those found as ‘high-risk’ be referred for colonoscopy.

A Framework for Leveraging "Big Data" to Advance Epidemiology and Improve Quality: Design of the VA Colonoscopy Collaborative.

Objective:To describe a framework for leveraging big data for research and quality improvement purposes and demonstrate implementation of the framework for design of the Department of Veterans Affairs (VA) Colonoscopy Collaborative.Methods:We propose that research utilizing large-scale electronic health records (EHRs) can be approached in a 4 step framework: 1) Identify data sources required to answer research question; 2) Determine whether variables are available as structured or free-text data; 3) Utilize a rigorous approach to refine variables and assess data quality; 4) Create the analytic dataset and perform analyses. We describe implementation of the framework as part of the VA Colonoscopy Collaborative, which aims to leverage big data to 1) prospectively measure and report colonoscopy quality and 2) develop and validate a risk prediction model for colorectal cancer (CRC) and high-risk polyps.Results:Examples of implementation of the 4 step framework are provided. To date, we have identified 2,337,171 Veterans who have undergone colonoscopy between 1999 and 2014. Median age was 62 years, and 4.6 percent (n = 106,860) were female. We estimated that 2.6 percent (n = 60,517) had CRC diagnosed at baseline. An additional 1 percent (n = 24,483) had a new ICD-9 code-based diagnosis of CRC on follow up.Conclusion:We hope our framework may contribute to the dialogue on best practices to ensure high quality epidemiologic and quality improvement work. As a result of implementation of the framework, the VA Colonoscopy Collaborative holds great promise for 1) quantifying and providing novel understandings of colonoscopy outcomes, and 2) building a robust approach for nationwide VA colonoscopy quality reporting.

Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies

ObjectiveTo systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts.DesignModels were identified through an update of a...

Prevalence and Penetrance of Major Genes and Polygenes for Colorectal Cancer.

Background: Although high-risk mutations in identified major susceptibility genes (DNA mismatch repair genes and MUTYH) account for some familial aggregation of colorectal cancer, their population prevalence and the causes of the remaining familial aggregation are not known.Methods: We studied the families of 5,744 colorectal cancer cases (probands) recruited from population cancer registries in the United States, Canada, and Australia and screened probands for mutations in mismatch repair genes and MUTYH We conducted modified segregation analyses using the cancer history of first-degree relatives, conditional on the proband's age at diagnosis. We estimated the prevalence of mutations in the identified genes, the prevalence of HR for unidentified major gene mutations, and the variance of the residual polygenic component.Results: We estimated that 1 in 279 of the population carry mutations in mismatch repair genes (MLH1 = 1 in 1,946, MSH2 = 1 in 2,841, MSH6 = 1 in 758, PMS2 = 1 in 714), 1 in 45 carry mutations in MUTYH, and 1 in 504 carry mutations associated with an average 31-fold increased risk of colorectal cancer in unidentified major genes. The estimated polygenic variance was reduced by 30% to 50% after allowing for unidentified major genes and decreased from 3.3 for age <40 years to 0.5 for age ≥70 years (equivalent to sibling relative risks of 5.1 to 1.3, respectively).Conclusions: Unidentified major genes might explain one third to one half of the missing heritability of colorectal cancer.Impact: Our findings could aid gene discovery and development of better colorectal cancer risk prediction models. Cancer Epidemiol Biomarkers Prev; 26(3); 404-12. ©2016 AACR.

Impact of a risk model based on routine lab results on colorectal cancer screening in average risk population.

1567 Background: Risk scores for colorectal cancer (CRC) screening use limited variables and have low predictive values. We sought to develop and validate a novel CRC risk prediction model based on routinely available data from electronic medical records including laboratory results. Methods: We conducted a nested case-control study using a population-based database. The cohort was randomly divided into test and validation datasets. Cases were defined as those with a diagnostic code of CRC, aged 50-85. Subjects with CRC syndromes or IBD were excluded. For every case, 4 controls matched on practice site and duration of follow-up were selected using incidence-density sampling. Lifestyle parameters, medical history, medications, and lab results were examined as CRC risk factors using univariate conditional logistic regression. Variables with p-value < 0.25 were evaluated for the multivariate model, after correcting for linearity, using backward elimination. Discrimination ability was calculated using receive...

Risk Prediction Model for Colorectal Cancer: National Health Insurance Corporation Study, Korea

PurposeIncidence and mortality rates of colorectal cancer have been rapidly increasing in Korea during last few decades. Development of risk prediction models for colorectal cancer in Korean men and women is urgently needed to enhance its prevention and early detection.MethodsGender specific five-year risk prediction models were developed for overall colorectal cancer, proximal colon cancer, distal colon cancer, colon cancer and rectal cancer. The model was developed using data from a population of 846,559 men and 479,449 women who participated in health examinations by the National Health Insurance Corporation. Examinees were 30–80 years old and free of cancer in the baseline years of 1996 and 1997. An independent population of 547,874 men and 415,875 women who participated in 1998 and 1999 examinations was used to validate the model. Model validation was done by evaluating its performance in terms of discrimination and calibration ability using the C-statistic and Hosmer-Lemeshow-type chi-square statistics.ResultsAge, body mass index, serum cholesterol, family history of cancer, and alcohol consumption were included in all models for men, whereas age, height, and meat intake frequency were included in all models for women. Models showed moderately good discrimination ability with C-statistics between 0.69 and 0.78. The C-statistics were generally higher in the models for men, whereas the calibration abilities were generally better in the models for women.ConclusionsColorectal cancer risk prediction models were developed from large-scale, population-based data. Those models can be used for identifying high risk groups and developing preventive intervention strategies for colorectal cancer.

10-Year risk of colorectal cancer: Development and validation of a prediction model in middle-aged Japanese men

Background: To estimate an individual's probability of developing colorectal cancer (CRC) may aid health professionals and individuals in improving lifestyle behaviors or deciding the screening regimens. As fewer studies on cancer risk prediction were seen so far, we initially developed an assessment tool with synthesizing key information from a variety of CRC risk factors through a large population-based cohort study. Method: The prediction model was derived from 28,115 men in the Japan Public Health Center-based (JPHC) Prospective Study Cohort II (follow-up: 1993–2005), with risk factors selected by Cox proportion hazard regression. 18,256 men in the JPHC Study Cohort I (follow-up: 1995–2005) were used to evaluate the model's performance. Results: 543 and 398 CRCs were diagnosed during the follow-up period in Cohorts II and I, respectively. The prediction model, including age, BMI, alcohol consumption, smoking status, and the daily physical activity level, showed modest discrimination ability for CRC ( C = 0.70; 95% confidential interval, 0.68–0.72) in Cohort II and well calibrated in Cohort I (Hosmer–Lemeshow χ 2 = 14.2, P = 0.08). Conclusion: The 10-year CRC risk prediction model may be used to estimate CRC risk in Japanese men. It may also play a role in the promotion of CRC prevention strategies.

Colorectal cancer risk prediction tool for men and women without known susceptibility

e15002 Background: Given the high incidence of colorectal cancer (CRC), and the availability of procedures that can detect disease and remove precancerous lesions, there is a need for a model that estimates the probability of developing CRC across various age intervals and risk factor profiles. Methods: The development of separate CRC absolute risk models for men and women included estimating relative risks and attributable risk parameters from population-based case-control data separately for proximal, distal, and rectal cancer and combining these estimates with baseline age-specific cancer hazard rates based on Surveillance, Epidemiology, and End Results (SEER) incidence rates and competing mortality risks. Results: For men, the model included a cancer- negative sigmoidoscopy/colonoscopy in the last 10 years, polyp history in the last 10 years, history of CRC in first-degree relatives, aspirin and nonsteroidal anti-inflammatory drug (NSAID) use, cigarette smoking, body mass index (BMI), current leisure-time vigorous activity, and vegetable consumption. For women, the model included sigmoidoscopy/colonoscopy, polyp history, history of CRC in first-degree relatives, aspirin and NSAID use, BMI, leisure-time vigorous activity, vegetable consumption, hormone-replacement therapy (HRT), and estrogen exposure on the basis of menopausal status. For men and women, relative risks differed slightly by tumor site. A validation study in independent data indicates that the models for men and women are well calibrated. Conclusions: We developed absolute risk prediction models for CRC from population-based data, and a simple questionnaire suitable for self-administration. This model is potentially useful for counseling, for designing research intervention studies, and for other applications. No significant financial relationships to disclose.

Laypersons' Responses to the Communication of Uncertainty Regarding Cancer Risk Estimates

To explore laypersons' responses to the communication of uncertainty associated with individualized cancer risk estimates and to identify reasons for individual differences in these responses. A qualitative study was conducted using focus groups. Participants were informed about a new colorectal cancer risk prediction model, and presented with hypothetical individualized risk estimates using presentation formats varying in expressed uncertainty (range v. point estimate). Semistructured interviews explored participants' responses to this information. Eight focus groups were conducted with 48 adults aged 50 to 74 residing in 2 major US metropolitan areas, Chicago, IL and Washington, DC. Purposive sampling was used to recruit participants with a high school or greater education, some familiarity with information technology, and no personal or immediate family history of cancer. Participants identified several sources of uncertainty regarding cancer risk estimates, including missing data, limitations in accuracy and source credibility, and conflicting information. In comparing presentation formats, most participants reported greater worry and perceived risk with the range than with the point estimate, consistent with the phenomenon of "ambiguity aversion.'' However, others reported the opposite effect or else indifference between formats. Reasons suggested by participants' responses included individual differences in optimism and motivations to reduce feelings of vulnerability and personal lack of control. Perceptions of source credibility and risk mutability emerged as potential mediating factors. Laypersons' responses to the communication of uncertainty regarding cancer risk estimates differ, and include both heightened and diminished risk perceptions. These differences may be attributable to personality, cognitive, and motivational factors.

Validation of a Colorectal Cancer Risk Prediction Model Among White Patients Age 50 Years and Older

Validation of an absolute risk prediction model for colorectal cancer (CRC) by using a large, population-based cohort. The National Institutes of Health (NIH) -American Association of Retired Persons (AARP) diet and health study, a prospective cohort study, was used to validate the model. Men and women age 50 to 71 years at baseline answered self-administered questionnaires that asked about demographic characteristics, diet, lifestyle, and medical histories. We compared expected numbers of CRC patient cases predicted by the model to the observed numbers of CRC patient cases identified in the NIH-AARP study overall and in subgroups defined by risk factor combinations. The discriminatory power was measured by the area under the receiver-operating characteristic curve (AUC). During an average of 6.9 years of follow-up, we identified 2,092 and 832 incident CRC patient cases in men and women, respectively. The overall expected/observed ratio was 0.99 (95% CI, 0.95 to 1.04) in men and 1.05 (95% CI, 0.98 to 1.11) in women. Agreement between the expected and the observed number of cases was good in most risk factor categories, except for in subgroups defined by CRC screening and polyp history. This discrepancy may be caused by differences in the question on screening and polyp history between two studies. The AUC was 0.61 (95% CI, 0.60 to 0.62) for men and 0.61 (95% CI, 0.59 to 0.62) for women, which was similar to other risk prediction models. The absolute risk model for CRC was well calibrated in a large prospective cohort study. This prediction model, which estimates an individual's risk of CRC given age and risk factors, may be a useful tool for physicians, researchers, and policy makers.

Colorectal Cancer Risk Prediction Tool for White Men and Women Without Known Susceptibility

Given the high incidence of colorectal cancer (CRC), and the availability of procedures that can detect disease and remove precancerous lesions, there is a need for a model that estimates the probability of developing CRC across various age intervals and risk factor profiles. The development of separate CRC absolute risk models for men and women included estimating relative risks and attributable risk parameters from population-based case-control data separately for proximal, distal, and rectal cancer and combining these estimates with baseline age-specific cancer hazard rates based on Surveillance, Epidemiology, and End Results (SEER) incidence rates and competing mortality risks. For men, the model included a cancer-negative sigmoidoscopy/colonoscopy in the last 10 years, polyp history in the last 10 years, history of CRC in first-degree relatives, aspirin and nonsteroidal anti-inflammatory drug (NSAID) use, cigarette smoking, body mass index (BMI), current leisure-time vigorous activity, and vegetable consumption. For women, the model included sigmoidoscopy/colonoscopy, polyp history, history of CRC in first-degree relatives, aspirin and NSAID use, BMI, leisure-time vigorous activity, vegetable consumption, hormone-replacement therapy (HRT), and estrogen exposure on the basis of menopausal status. For men and women, relative risks differed slightly by tumor site. A validation study in independent data indicates that the models for men and women are well calibrated. We developed absolute risk prediction models for CRC from population-based data, and a simple questionnaire suitable for self-administration. This model is potentially useful for counseling, for designing research intervention studies, and for other applications.