Validation of a colorectal cancer risk prediction model among white patients age 50 years and older

Risk projection for time-to-event outcome from population-based case-control studies leveraging summary statistics from the target population

Risk stratification based on prediction models has become increasingly important in preventing and managing chronic diseases. However, due to cost- and time-limitations, not every population can have resources for collecting enough detailed individual-level information on a large number of people to develop risk prediction models. A more practical approach is to use prediction models developed from existing studies and calibrate them with relevant summary-level information of the target population. Many existing studies were conducted under the population-based case-control design. Gail et al. (J Natl Cancer Inst 81:1879-1886, 1989) proposed to combine the odds ratio estimates obtained from case-control data and the disease incidence rates from the target population to obtain the baseline hazard function, and thereby the pure risk for developing diseases. However, the approach requires the risk factor distribution of cases from the case-control studies be same as the target population, which, if violated, may yield biased risk estimation. In this article, we propose two novel weighted estimating equation approaches to calibrate the baseline risk by leveraging the summary information of (some) risk factors in addition to disease-free probabilities from the targeted population. We establish the consistency and asymptotic normality of the proposed estimators. Extensive simulation studies and an application to colorectal cancer studies demonstrate the proposed estimators perform well for bias reduction in finite samples.

Optimal timing of a colonoscopy screening schedule depends on adenoma detection, adenoma risk, adherence to screening and the screening objective: A microsimulation study

Colonoscopy-based screening provides protection against colorectal cancer (CRC), but the optimal starting age and time intervals of screening colonoscopies are unknown. We aimed to determine an optimal screening schedule for the US population and its dependencies on the objective of screening (life years gained or incidence, mortality, or cost reduction) and the setting in which screening is performed. We used our established open-source microsimulation model CMOST to calculate optimized colonoscopy schedules with one, two, three or four screening colonoscopies between 20 and 90 years of age. A single screening colonoscopy was most effective in reducing life years lost from CRC when performed at 55 years of age. Two, three and four screening colonoscopy schedules saved a maximum number of life years when performed between 49-64 years; 44-69 years; and 40-72 years; respectively. However, for maximum incidence and mortality reduction, screening colonoscopies needed to be scheduled 4-8 years later in life. The optimum was also influenced by adenoma detection efficiency with lower values for these parameters favoring a later starting age of screening. Low adherence to screening consistently favored a later start and an earlier end of screening. In a personalized approach, optimal screening would start earlier for high-risk patients and later for low-risk individuals. In conclusion, our microsimulation-based approach supports colonoscopy screening schedule between 45 and 75 years of age but the precise timing depends on the objective of screening, as well as assumptions regarding individual CRC risk, efficiency of adenoma detection during colonoscopy and adherence to screening.

Application of machine-learning model to optimize colonic adenoma detection in India

AIMS

There is limited data on the prevalence and risk factors of colonic adenoma from the Indian sub-continent. We aimed at developing a machine-learning model to optimize colonic adenoma detection in a prospective cohort.

METHODS

All consecutive adult patients undergoing diagnostic colonoscopy were enrolled between October 2020 and November 2022. Patients with a high risk of colonic adenoma were excluded. The predictive model was developed using the gradient-boosting machine (GBM)-learning method. The GBM model was optimized further by adjusting the learning rate and the number of trees and 10-fold cross-validation.

RESULTS

Total 10,320 patients (mean age 45.18 ± 14.82 years; 69% men) were included in the study. In the overall population, 1152 (11.2%) patients had at least one adenoma. In patients with age > 50 years, hospital-based adenoma prevalence was 19.5% (808/4144). The area under the receiver operating curve (AUC) (SD) of the logistic regression model was 72.55% (4.91), while the AUCs for deep learning, decision tree, random forest and gradient-boosted tree model were 76.25% (4.22%), 65.95% (4.01%), 79.38% (4.91%) and 84.76% (2.86%), respectively. After model optimization and cross-validation, the AUC of the gradient-boosted tree model has increased to 92.2% (1.1%).

CONCLUSIONS

Machine-learning models may predict colorectal adenoma more accurately than logistic regression. A machine-learning model may help optimize the use of colonoscopy to prevent colorectal cancers.

TRIAL REGISTRATION

ClinicalTrials.gov (ID: NCT04512729).

Prediction of the 4-Year Incidence Risk of Ischemic Stroke in Healthy Japanese Adults: The Fukushima Health Database

AIM

Estimating the risk of developing ischemic stroke (IS) may assist health professionals in motivating individuals to modify their risk behavior.

METHODS

A predictive model was derived from 178,186 participants from Fukushima Health Database, aged 40-74 years, who attended the health checkup in 2014 and completed at least one annual health checkup by 2018 (Cohort I). Cox proportional hazard regression model was used to build a 4-year prediction model, thus the risk scores were based on the regression coefficients. External validation for the risk scores was conducted in another cohort of 46,099 participants following between 2015 and 2019 (Cohort II).

RESULTS

The 4-year cumulated incidence rate of IS was 179.80/100,000 person-years in Cohort I. The predictive model included age, sex, blood pressure, hypertension treatment, diabetes, low- and high-density lipoprotein cholesterol, smoking, walking pace, and body weight change of 3 kg within one year. Risk scores were interpreted based on the Cohort I predictive model function. The Harrell's C-statistics of the discrimination ability of the risk score model (95% confidence interval) was 0.744 (0.729-0.759) in Cohort I and 0.770 (0.743-0.797) in Cohort II. The overall agreement of the risk score probability of IS incidence for the observed/expected case ratio and 95% CI was 0.98 (0.92-1.05) in Cohort I and 1.08 (0.95-1.22) in Cohort II.

CONCLUSIONS

The 4-year risk prediction model revealed a good performance for IS incidence, and risk scores could be used to estimate individual incidence risk of IS. Updated models with additional confirmed risk variables may be needed.

Colorectal Cancer Risk Assessment and Precision Approaches to Screening: Brave New World or Worlds Apart?

Current colorectal cancer (CRC) screening recommendations take a "one-size-fits-all" approach using age as the major criterion to initiate screening. Precision screening that incorporates factors beyond age to risk stratify individuals could improve on current approaches and optimally use available resources with benefits for patients, providers, and health care systems. Prediction models could identify high-risk groups who would benefit from more intensive screening, while low-risk groups could be recommended less intensive screening incorporating noninvasive screening modalities. In addition to age, prediction models incorporate well-established risk factors such as genetics (eg, family CRC history, germline, and polygenic risk scores), lifestyle (eg, smoking, alcohol, diet, and physical inactivity), sex, and race and ethnicity among others. Although several risk prediction models have been validated, few have been systematically studied for risk-adapted population CRC screening. In order to envisage clinical implementation of precision screening in the future, it will be critical to develop reliable and accurate prediction models that apply to all individuals in a population; prospectively study risk-adapted CRC screening on the population level; garner acceptance from patients and providers; and assess feasibility, resources, cost, and cost-effectiveness of these new paradigms. This review evaluates the current state of risk prediction modeling and provides a roadmap for future implementation of precision CRC screening.

Using Online Colorectal Cancer Risk Calculators to Guide Screening Decision-Making

BACKGROUND

Several online calculators estimate colorectal cancer risk, but their consistency is unknown. Our objectives were to quantify the variation in predicted risk and to determine which calculators are best used in the clinical setting.

METHODS

We used the Google search engine to identify online colorectal cancer risk calculators and assessed the output of each for 3 hypothetical screening scenarios (low-, average-, and high-risk), varied by age (50, 62, 75 years), sex, and race (Black, White), with risk levels based on risk-appropriate values for variables in each model. Estimated risks for models within a given scenario were rated as consistent or inconsistent based on comparison with either the absolute magnitude of difference or average lifetime risk of colorectal cancer. Summary statistics for consistent and inconsistent estimates were compared using chi-square and Fisher's exact tests.

RESULTS

We identified 5 online colorectal cancer risk calculators. Inconsistencies were found in none of 5-year, 19% of 10-year, and 81% of lifetime colorectal cancer risk estimate comparisons (P < .001). For a 50-year-old, 22% of risk estimate comparisons were inconsistent, vs 33% for a 62-year-old, and 36% for a 75-year-old (P = 0.14).

CONCLUSIONS

Online colorectal cancer risk models are more consistent in predicting colorectal cancer risk for 5- and 10-year time frames compared with lifetime. For a US population, the National Cancer Institute's Colorectal Cancer Risk Assessment Tool is a rigorously developed calculator that can be used in the clinical setting to provide 5-year and lifetime risk estimates.

Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review

The inclusion of machine-learning-derived models in systematic reviews of risk prediction models for colorectal cancer is rare. Whilst such reviews have highlighted methodological issues and limited performance of the models included, it is unclear why machine-learning-derived models are absent and whether such models suffer similar methodological problems. This scoping review aims to identify machine-learning models, assess their methodology, and compare their performance with that found in previous reviews. A literature search of four databases was performed for colorectal cancer prediction and prognosis model publications that included at least one machine-learning model. A total of 14 publications were identified for inclusion in the scoping review. Data was extracted using an adapted CHARM checklist against which the models were benchmarked. The review found similar methodological problems with machine-learning models to that observed in systematic reviews for non-machine-learning models, although model performance was better. The inclusion of machine-learning models in systematic reviews is required, as they offer improved performance despite similar methodological omissions; however, to achieve this the methodological issues that affect many prediction models need to be addressed.

Construction and Validation of a Protein-associated Prognostic Model for Gastrointestinal Cancer

Background Gastrointestinal cancer (GIC) is a prevalent and lethal malignant tumor. It is obligatory to investigate innovative biomarkers for the diagnosis and prognosis. Proteins play a crucial role in regulating the occurrence and progression of GIC. However, the prognostic value of proteins is unclear in GIC.

OBJECTIVE

This paper aims to identify the hub prognosis-related proteins (PAPs) and construct a prognosis model for GIC patients for clinical application.

METHODS

Protein expression data of GIC was obtained from The Cancer Proteome Atlas (TCPA) and downloaded the clinicopathological data from The Cancer Genome Atlas database (TCGA). Besides, hub proteins were filtrated via univariate and multivariate Cox regression analysis. Moreover, survival analysis and nomogram were used to predict overall survival (OS). We used the calibration curves to assess the consistency of predictive and actual survival rates. The consistency index (C-index) was used to evaluate the prognostic ability of the predictive model. Furthermore, functional enrichment analysis and protein co-expression of PAPs were used to explore their roles in GIC.

RESULTS

Finally, a prognosis model was conducted based on ten PAPs (CYCLIND1, DVL3, NCADHERIN, SYK, ANNEXIN VII, CD20, CMET, RB, TFRC, and PREX1). The risk score calculated by the model was an independent prognostic predictor. Compared with the high-risk subgroup, the low-risk subgroup had better OS. In the TCGA cohort, the area under the curve value of the receiver operating characteristic curve of the prognostic model was 0.692. The expression of proteins and risk score had a significant association with the clinicopathological characteristics of GIC. Besides, a nomogram based on GIC clinicopathological features and risk scores could properly predict the OS of individual GIC patients. The C-index is 0.71 in the TCGA cohort and 0.73 in the GEO cohort.

CONCLUSION

The results indicate that the risk score is an independent prognostic biomarker and is related to the malignant clinical features of GIC patients. Besides, several PAPs associated with the survival and clinicopathological characteristics of GIC might be potential biomarkers for GIC diagnosis and treatment.

Utility of machine learning in developing a predictive model for early-age-onset colorectal neoplasia using electronic health records

BACKGROUND AND AIMS

The 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).

RESULTS

The 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<0.0015]. The most important predictive variables in the regularized discriminant analysis model for CRC or high-risk polyps were income per zip code, the colonoscopy indication, and body mass index quartiles.

DISCUSSION

Machine learning can predict CRC risk in adults aged 35-50 using EHR with improved discrimination. Further development of our model is needed, followed by validation in a primary-care setting, before clinical application.

Five-year absolute risk estimates of colorectal cancer based on CCRAT model and polygenic risk scores: A validation study using the Quebec population-based cohort CARTaGENE

The objective was to evaluate the predictive performance of the Colorectal Cancer Risk Assessment Tool (CCRAT) and three polygenic risk scores (Hsu et al., 2015; Law et al., 2019, Archambault et al., 2020) to predict the occurrence of colorectal cancer at five years in a Quebec population-based cohort. By using the CARTaGENE cohort, we computed the absolute risk of colorectal cancer with the CCRAT model, the polygenic risk scores (PRS) and combined clinico-genetic models (CCRAT + PRS). We also tailored the CCRAT model by using the marginal age-specific colorectal incidence rates in Canada and the risk score distribution. We reported the calibration and the discrimination. Performances of the PRSs, combined and tailored CCRAT models were compared to the original CCRAT model. The expected-to-observed ratio of the original CCRAT model was 0.54 [0.43-0.68]. The c-index was 74.79 [68.3-80.5]. The tailored CCRAT model improved the expected-to-observed ratio (0.74 [0.59-0.94]) and c-index (76.39 [69.7-82.1]). All PRS improved the expected-to-observed ratios (around 0.83, confidence intervals including one). PRSs' c-indexes were not significantly different from CCRAT models. Results from the combined models were close to those from the PRS models, Archambault combined model's c-index being significantly higher than the original and tailored CCRAT models (78.67 [70.8-86.5]; p < 0.001 and p = 0.028, respectively). In this Quebec cohort, CCRAT model has a good discrimination with a poor calibration. While the tailored CCRAT provides some gain in calibration, clinico-genetic models improved both calibration and discrimination. However, better calibrations must be obtained before a practical use among the inhabitants of Quebec province.

Prediction Model Using Readily Available Clinical Data for Colorectal Cancer in Chinese Population

BACKGROUND

In China, health screening has become common, although colonoscopy is not always available or acceptable. We sought to develop a prediction model of colorectal cancer (CRC) for health screening population based on readily available clinical data to reduce labor and economic costs.

METHODS

We conducted a cross-sectional study based on a health screening population in Karamay Central Hospital. By collecting clinical data and basic information from participants, we identified independent risk factors and established a prediction model of CRC. Internal and external validation, calibration plot, and decision curve analysis were employed to test discriminating ability, calibration ability, and clinical practicability.

RESULTS

Independent risk factors of CRC, which were readily available in basic public health institutions, included high-density lipoprotein cholesterol, male sex, total cholesterol, advanced age, and hemoglobin. These factors were successfully incorporated into the prediction model (AUC 0.740, 95% CI 0.713-0.767). The model demonstrated a high degree of discrimination and calibration, in addition to a high degree of clinical practicability in high-risk people.

CONCLUSIONS

The prediction model exhibits good discrimination and calibration and is pragmatic for CRC screening in rural areas and basic public health institutions.

Designing a Predictive Model for Colorectal Neoplasia Diagnosis Based on Clinical and Laboratory Findings in Colonoscopy Candidate Patients

BACKGROUND

Health authorities have expanded two strategies to diminish CRC-related influence: CR screening and improve diagnostic process in symptomatic patients. The aim of the current study is to design a predictive model to identify the most important risk factors that can efficiently predict patients who have high risk of colorectal neoplasia.

METHOD

A cross-sectional study was constructed to include all patients who had positive test for FIT or had one or more risk factors for colorectal cancer based on the guidelines of detecting high-risk groups for colorectal cancer in Iran. Multivariable binary logistic regression model was constructed for prediction of colorectal neoplasia. We used sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratio to check the accuracy. The Hosmer-Lemeshow test, chi-square test, and p value were used to determine the precision of model.

RESULT

Following an AIC stepwise selection model, only nine potential variables, namely gender, watery diarrhea, IBD, abdominal pain, melena, body mass index, depression drug, anti-inflammatory drug, and age, were found to be a predictor of colorectal neoplasia. The best cut-point probability in the final model was 0.27 and results of sensitivity and specificity, based on maximizing these two criteria, were 66% and 62%, respectively.

CONCLUSION

Overall, our model prediction was comparable with other risk prediction models for colorectal cancer. It had a modest discriminatory power to distinguish an individual's neoplasia colorectal risk.

Alterations, Interactions, and Diagnostic Potential of Gut Bacteria and Viruses in Colorectal Cancer

Gut microbiome alteration was closely associated with colorectal cancer (CRC). Previous studies had demonstrated the bacteria composition changes but lacked virome profiles, trans-kindom interactions, and reliable diagnostic model explorations in CRC. Hence, we performed metagenomic sequencing to investigate the gut microbiome and microbial interactions in adenoma and CRC patients. We found the decreased microbial diversity in CRC and revealed the taxonomic alterations of bacteria and viruses were highly associated with CRC at the species level. The relative abundance of oral-derived species, such as Fusobacterium nucleatum, Fusobacterium hwasookii, Porphyromonas gingivalis, and Bacteroides fragilis, increased. At the same time, butyrate-producing and anti-inflammatory microbes decreased in adenoma and CRC by non-parametric Kruskal-Wallis test. Despite that, the relative abundance of Escherichia viruses and Salmonella viruses increased, whereas some phages, including Enterobacteria phages and Uncultured crAssphage, decreased along with CRC development. Gut bacteria was negatively associated with viruses in CRC and healthy control by correlation analysis (P=0.017 and 0.002, respectively). Viruses were much more dynamic than the bacteria as the disease progressed, and the altered microbial interactions were distinctively stage-dependent. The degree centrality of microbial interactions decreased while closeness centrality increased along with the adenoma to cancer development. Uncultured crAssphage was the key bacteriophage that enriched in healthy controls and positively associated with butyrate-producing bacteria. Diagnostic tests based on bacteria by random forest confirmed in independent cohorts showed better performance than viruses for CRC. In conclusion, our study revealed the novel CRC-associated bacteria and viruses that exhibited specific differences and intensive microbial correlations, which provided a reliable diagnostic panel for CRC.

Future Incidence of Malignant Mesothelioma in South Korea: Updated Projection to 2038

Malignant mesothelioma (MM) is a cancer that is largely caused by exposure to asbestos. Although asbestos is no longer used in South Korea, the incidence of MM continues to increase due to its long latent period. We aimed to update the previous prediction of MM incidence until 2038. We predicted the incidence of MM over the next 20 years (2019–2038) in South Korea using Møller’s age–period–cohort (APC) model and a Poisson regression model based on asbestos consumption. The APC model predicted that the crude incidence rate would increase sharply in men and slowly in women. Despite the sex discrepancy in the rate of increase, the incidence rate for both sexes is expected to continue increasing until 2038. In the Poisson model, the crude incidence rate was predicted to increase continuously until 2038, and far more cases of MM were predicted to occur compared with the results of the APC model. When compared with actual incidence data, the APC model was deemed more suitable than the Poisson model. The APC model predicted a continuous increase over the next 20 years with no peak, suggesting that the incidence of MM will continue to rise far into the future.

Derivation and validation of a predictive model for advanced colorectal neoplasia in asymptomatic adults

OBJECTIVE

Knowing risk for advanced colorectal neoplasia (AN) could help patients and providers choose among screening tests, improving screening efficiency and uptake. We created a risk prediction model for AN to help decide which test might be preferred, a use not considered for existing models.

DESIGN

Average-risk 50-to-80-year olds undergoing first-time screening colonoscopy were recruited from endoscopy units in Indiana. We measured sociodemographic and physical features, medical and family history and lifestyle factors and linked these to the most advanced finding. We derived a risk equation on two-thirds of the sample and assigned points to each variable to create a risk score. Scores with comparable risks were collapsed into risk categories. The model and score were tested on the remaining sample.

RESULTS

Among 3025 subjects in the derivation set (mean age 57.3 (6.5) years; 52% women), AN prevalence was 9.4%. The 13-variable model (c-statistic=0.77) produced three risk groups with AN risks of 1.5% (95% CI 0.72% to 2.74%), 7.06% (CI 5.89% to 8.38%) and 27.26% (CI 23.47% to 31.30%) in low-risk, intermediate-risk and high-risk groups (p value <0.001), containing 23%, 59% and 18% of subjects, respectively. In the validation set of 1475 subjects (AN prevalence of 8.4%), model performance was comparable (c-statistic=0.78), with AN risks of 2.73% (CI 1.25% to 5.11%), 5.57% (CI 4.12% to 7.34%) and 25.79% (CI 20.51% to 31.66%) in low-risk, intermediate-risk and high-risk subgroups, respectively (p<0.001), containing proportions of 23%, 59% and 18%.

CONCLUSION

Among average-risk persons, this model estimates AN risk with high discrimination, identifying a lower risk subgroup that may be screened non-invasively and a higher risk subgroup for which colonoscopy may be preferred. The model could help guide patient-provider discussions of screening options, may increase screening adherence and conserve colonoscopy resources.

Risk prediction models for colorectal cancer: Evaluating the discrimination due to added biomarkers

Most risk prediction models for colorectal cancer (CRC) are based on questionnaires and show a modest discriminatory ability. Therefore, we aim to develop risk prediction models incorporating plasma biomarkers for CRC to improve discrimination. We assessed the predictivity of 11 biomarkers in 736 men in the Health Professionals Follow-up Study and 639 women in the Nurses' Health Study. We used stepwise logistic regression to examine whether a set of biomarkers improved the predictivity on the basis of predictors in the National Cancer Institute's (NCI) Colorectal Cancer Risk Assessment Tool. Model discrimination was assessed using C-statistics. Bootstrap with 500 randomly sampled replicates was used for internal validation. The models containing each biomarker generated a C-statistic ranging from 0.50 to 0.59 in men and 0.50 to 0.54 in women. The NCI model demonstrated a C-statistic (95% CI) of 0.67 (0.62-0.71) in men and 0.58 (0.54-0.63) in women. Through stepwise selection of biomarkers, the C-statistic increased to 0.70 (0.66-0.74) in men after adding growth/differentiation factor 15, total adiponectin, sex hormone binding globulin and tumor necrosis factor receptor superfamily member 1B (P for difference = 0.008); and increased to 0.62 (0.57-0.66) in women after further including insulin-like growth factor 1 and insulin-like growth factor-binding protein 3 (P for difference = .06). The NCI + selected biomarkers model was internally validated with a C-statistic (95% CI) of 0.73 (0.70-0.77) in men and 0.66 (0.61-0.70) in women. Circulating plasma biomarkers may improve the performance of risk factor-based prediction model for CRC.

Randomized Controlled Trial of Personalized Colorectal Cancer Risk Assessment vs Education to Promote Screening Uptake

INTRODUCTION

Risk stratification has been proposed as a strategy to improve participation in colorectal cancer (CRC) screening, but evidence is lacking. We performed a randomized controlled trial of risk stratification using the National Cancer Institute's Colorectal Cancer Risk Assessment Tool (CCRAT) on screening intent and completion.

METHODS

A total of 230 primary care patients eligible for first-time CRC screening were randomized to risk assessment via CCRAT or education control. Follow-up of screening intent and completion was performed by record review and phone at 6 and 12 months. We analyzed change in intent after intervention, time to screening, overall screening completion rates, and screening completion by CCRAT risk score tertile.

RESULTS

Of the patients, 61.7% of patients were aged <60 years, 58.7% female, and 94.3% with college or higher education. Time to screening did not differ between arms (hazard ratio 0.78 [95% confidence interval (CI) 0.52-1.18], P = 0.24). At 12 months, screening completion was 38.6% with CCRAT vs 44.0% with education (odds ratio [OR] 0.80 [95% CI 0.47-1.37], P = 0.41). Changes in screening intent did not differ between the risk assessment and education arms (precontemplation to contemplation: OR 1.52 [95% CI 0.81-2.86], P = 0.19; contemplation to precontemplation: OR 1.93 [95% CI 0.45-8.34], P = 0.38). There were higher screening completion rates at 12 months in the top CCRAT risk tertile (52.6%) vs the bottom (32.4%) and middle (31.6%) tertiles (P = 0.10).

DISCUSSION

CCRAT risk assessment did not increase screening participation or intent. Risk stratification might motivate persons classified as higher CRC risk to complete screening, but unintentionally discourage screening among persons not identified as higher risk.

Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score

BACKGROUND

Nutrition and lifestyle have been long established as risk factors for colorectal cancer (CRC). Modifiable lifestyle behaviours bear potential to minimize long-term CRC risk; however, translation of lifestyle information into individualized CRC risk assessment has not been implemented. Lifestyle-based risk models may aid the identification of high-risk individuals, guide referral to screening and motivate behaviour change. We therefore developed and validated a lifestyle-based CRC risk prediction algorithm in an asymptomatic European population.

METHODS

The model was based on data from 255,482 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) study aged 19 to 70 years who were free of cancer at study baseline (1992-2000) and were followed up to 31 September 2010. The model was validated in a sample comprising 74,403 participants selected among five EPIC centres. Over a median follow-up time of 15 years, there were 3645 and 981 colorectal cancer cases in the derivation and validation samples, respectively. Variable selection algorithms in Cox proportional hazard regression and random survival forest (RSF) were used to identify the best predictors among plausible predictor variables. Measures of discrimination and calibration were calculated in derivation and validation samples. To facilitate model communication, a nomogram and a web-based application were developed.

RESULTS

The final selection model included age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary. The risk score demonstrated good discrimination overall and in sex-specific models. Harrell's C-index was 0.710 in the derivation cohort and 0.714 in the validation cohort. The model was well calibrated and showed strong agreement between predicted and observed risk. Random survival forest analysis suggested high model robustness. Beyond age, lifestyle data led to improved model performance overall (continuous net reclassification improvement = 0.307 (95% CI 0.264-0.352)), and especially for young individuals below 45 years (continuous net reclassification improvement = 0.364 (95% CI 0.084-0.575)).

CONCLUSIONS

LiFeCRC score based on age and lifestyle data accurately identifies individuals at risk for incident colorectal cancer in European populations and could contribute to improved prevention through motivating lifestyle change at an individual level.

AGA White Paper: Roadmap for the Future of Colorectal Cancer Screening in the United States

The American Gastroenterological Association's Center for Gastrointestinal Innovation and Technology convened a consensus conference in December 2018, entitled, "Colorectal Cancer Screening and Surveillance: Role of Emerging Technology and Innovation to Improve Outcomes." The goal of the conference, which attracted more than 60 experts in screening and related disciplines, including the authors, was to envision a future in which colorectal cancer (CRC) screening and surveillance are optimized, and to identify barriers to achieving that future. This White Paper originates from that meeting and delineates the priorities and steps needed to improve CRC outcomes, with the goal of minimizing CRC morbidity and mortality. A one-size-fits-all approach to CRC screening has not and is unlikely to result in increased screening uptake or desired outcomes owing to barriers stemming from behavioral, cultural, and socioeconomic causes, especially when combined with inefficiencies in deployment of screening technologies. Overcoming these barriers will require the following: efficient utilization of multiple screening modalities to achieve increased uptake; continued development of noninvasive screening tests, with iterative reassessments of how best to integrate new technologies; and improved personal risk assessment to better risk-stratify patients for appropriate screening testing paradigms.

Identification of prognosis-associated immune genes and exploration of immune cell infiltration in colorectal cancer

Aim: To identify prognosis-related immune genes (PRIGs) and construct a prognosis model of colorectal cancer (CRC) patients for clinical use. Materials & methods: Expression profiles were obtained from The Cancer Genome Atlas database and identified differentially expressed PRIGs of CRC. Results: A prognostic model was conducted based on nine PRIGs. The risk score, based on prognosis model, was an independent prognostic predictor. Five PRIGs and risk score were significantly associated with the clinical stage of CRC and five immune cells related to the risk score. Conclusion: The risk score was an independent prognostic biomarker for CRC patients. The research excavated immune genes that were associated with survival and that could be potential biomarkers for prognosis and treatment for CRC patients.

Evaluating the predictive performance of malaria antibodies and FCGR3B gene polymorphisms on Plasmodium falciparum infection outcome: a prospective cohort study

Background

Malaria antigen-specific antibodies and polymorphisms in host receptors involved in antibody functionality have been associated with different outcomes of Plasmodium falciparum infections. Thus, to identify key prospective malaria antigens for vaccine development, there is the need to evaluate the associations between malaria antibodies and antibody dependent host factors with more rigorous statistical methods. In this study, different statistical models were used to evaluate the predictive performance of malaria-specific antibodies and host gene polymorphisms on P. falciparum infection in a longitudinal cohort study involving Ghanaian children.

Methods

Models with different functional forms were built using known predictors (age, sickle cell status, blood group status, parasite density, and mosquito bed net use) and malaria antigen-specific immunoglobulin (Ig) G and IgG subclasses and FCGR3B polymorphisms shown to mediate antibody-dependent cellular functions. Malaria antigens studied were Merozoite surface proteins (MSP-1 and MSP-3), Glutamate Rich Protein (GLURP)-R0, R2, and the Apical Membrane Antigen (AMA-1). The models were evaluated through visualization and assessment of differences between the Area Under the Receiver Operating Characteristic Curve and Brier Score estimated by suitable internal cross-validation designs.

Results

This study found that the FCGR3B-c.233C>A genotype and IgG against AMA1 were relatively better compared to the other antibodies and FCGR3B genotypes studied in classifying or predicting malaria risk among children.

Conclusions

The data supports the P. falciparum, AMA1 as an important malaria vaccine antigen, while FCGR3B-c.233C>A under the additive and dominant models of inheritance could be an important modifier of the effect of malaria protective antibodies.

Chronic non-communicable disease risk calculators - An overview, part I

This review identifies the different risk assessment tools that stratify the individual's risk of four of the eight leading causes of death in women: breast cancer, lung cancer, colorectal cancer and osteoporosis. It will be followed by the publication of a second paper that summarizes the risk assessment tools for the other four leading causes of death (myocardial infarction, stroke, diabetes mellitus type 2 and dementia). The different tools were compared by their use of different variables and validation criteria. To corroborate the validation process, validation study papers were considered for each risk assessment tool. Four tables, one for each illness, were designed. The tables provide an outline for each risk assessment tool, which includes its inventor/company, required variables, advantages, disadvantages and validity. These tables simplify the comparison of the different tools and enable the identification of the most suitable one for each patient.

Copy number variation of ubiquitin- specific proteases genes in blood leukocytes and colorectal cancer

Ubiquitin-specific proteases (USPs) play important roles in the regulation of many cancer-related biological processes. USPs copy number variation (CNVs) may affect the risk and prognosis of colorectal cancer (CRC). We detected CNVs of USPs genes in 468 matched CRC patients and controls, estimated the associations between the USPs genes CNVs and CRC risk and prognosis and their interactions with environmental factors on CRC risk. Finally, we generated five CRC risk predictive models with different CNVs patterns combining with environmental factors (EF). We identified significant association between CYLD deletion and CRC risk (ORadj = 4.18, 95% CI: 2.03-8.62), significant association between USP9X amplification and CRC risk (ORadj = 2.30, 95% CI: 1.48-3.57), and significant association between USP11 deletion and CRC risk (ORadj = 3.49, 95% CI: 1.49-8.64). There were significant gene-environment and gene-gene interactions on CRC risk. The area under the receiver operating characteristic curve (AUC) of EF + SIG (deletion of CYLD and USP11, amplification of USP9X) model was significantly larger than any other models (AUC = 0.75, 95% CI: 0.74-0.77). We did not identify significant associations between CNVs of the three genes and CRC prognosis. CNVs of CYLD, USP9X, and USP11 are significantly associated with the risk of CRC. Gene-gene and gene-environment interactions might also play an important role in the development of CRC.

Development of a comprehensive health-risk prediction tool for postmenopausal women

OBJECTIVE

The aim of the study was to develop a web-based calculator that predicts the likelihood of experiencing multiple, competing outcomes prospectively over 5, 10, and 15 years.

METHODS

Baseline demographic and medical data from a healthy and racially and ethnically diverse cohort of 161,808 postmenopausal women, aged 50 to 79 at study baseline, who participated in the Women's Health Initiative (WHI), were used to develop and evaluate a risk-prediction calculator designed to predict individual risk for morbidity and mortality outcomes. Women were enrolled from 40 sites arranged in four regions of the United States. The calculator predicts all-cause mortality, adjudicated outcomes of health events (ie, myocardial infarction [MI], stroke, and hip fracture), and disease (lung, breast, and colorectal cancer). A proportional subdistribution hazards regression model was used to develop the calculator in a training dataset using data from three regions. The calculator was evaluated using the C-statistic in a test dataset with data from the fourth region.

RESULTS

The predictive validity of our calculator measured by the C-statistic in the test dataset for a first event at 5 and 15 years was as follows: MI 0.77, 0.61, stroke 0.77, 0.72, lung cancer 0.82, 0.79, breast cancer 0.60, 0.59, colorectal cancer 0.67, 0.60, hip fracture 0.79, 0.76, and death 0.74, 0.72.

CONCLUSION

This study represents the first large-scale study to develop a risk prediction calculator that yields health risk prediction for several outcomes simultaneously. Development of this tool is a first step toward enabling women to prioritize interventions that may decrease these risks. : Video Summary:http://links.lww.com/MENO/A463.

A risk prediction tool for colorectal cancer screening: a qualitative study of patient and provider facilitators and barriers

BACKGROUND

Despite proven effectiveness of colorectal cancer (CRC) screening, at least 35% of screen-eligible adults are not current with screening. Decision aids and risk prediction tools may help increase uptake, adherence, and efficiency of CRC screening by presenting lower-risk patients with options less invasive than colonoscopy. The purpose of this qualitative study was to determine patient and provider perceptions of facilitators and barriers to use of a risk prediction tool for advanced colorectal neoplasia (CRC and advanced, precancerous polyps), to maximize its chances of successful clinical implementation.

METHODS

We conducted qualitative, semi-structured interviews with patients aged 50-75 years who were not current with CRC screening, and primary care providers (PCPs) at an academic and a U.S. Department of Veterans Affairs Medical Center in the Midwest from October 2016 through March 2017. Participants were asked about their current experiences discussing CRC screening, then were shown the risk tool and asked about its acceptability, barriers, facilitators, and whether they would use it to guide their choice of a screening test. The constant comparative method guided analysis.

RESULTS

Thirty patients and PCPs participated. Among facilitators were the tool's potential to increase screening uptake, reduce patient risk, improve resource allocation, and facilitate discussion about CRC screening. PCP-identified barriers included concerns about the tool's accuracy, consistency with guidelines, and time constraints.

CONCLUSIONS

Patients and PCPs found the risk prediction tool useful, with potential to increase uptake, safety, and efficiency of CRC screening, indicating potential acceptability and feasibility of implementation into clinical practice.

Colorectal cancer outcomes after screening with the multi-target stool DNA assay: protocol for a large-scale, prospective cohort study (the Voyage study)

Introduction

Population-level screening has been shown to reduce the incidence and mortality of colorectal cancer (CRC). Unfortunately, adherence to screening recommendations among eligible US adults remains below national goals. A relatively new non-invasive screening modality, the Food and Drug Administration-approved multi-target stool DNA (mt-sDNA) assay (commercialised as Cologuard), which combines the detection of haemoglobin and DNA abnormalities, has been completed by more than 3 million individuals. Given mt-sDNA's recent availability, the effectiveness of mt-sDNA screening with respect to CRC incidence and mortality reduction has not yet been established.

Methods and analysis

Through an academic-industry collaboration, a prospective cohort study (Voyage) was designed with an initial enrolment target of 150 000 individuals with mt-sDNA ordered by their healthcare provider for CRC screening. Consented participants will be asked to complete a baseline questionnaire to collect sociodemographic and health information. Additional questionnaires will be provided after 1 year, and every 3 years thereafter, to collect data regarding CRC screening follow-up in order to estimate rates of CRC incidence and other health outcomes. Linkage to the National Death Index will be used to estimate mortality rates.

Ethics and dissemination

The Voyage study will be conducted in accordance with international guidelines and local regulatory requirements and laws. Data will be stored and retained at Mayo Clinic. Only limited data elements required for research purposes will be transmitted between Mayo Clinic and Exact Sciences Laboratories. Results of the Voyage study will be disseminated through scientific presentations and publications.

Trial registration number

NCT04124406.

Prognostic implications of metabolism-associated gene signatures in colorectal cancer

Colorectal cancer (CRC) is one of the most common and deadly malignancies. Novel biomarkers for the diagnosis and prognosis of this disease must be identified. Besides, metabolism plays an essential role in the occurrence and development of CRC. This article aims to identify some critical prognosis-related metabolic genes (PRMGs) and construct a prognosis model of CRC patients for clinical use. We obtained the expression profiles of CRC from The Cancer Genome Atlas database (TCGA), then identified differentially expressed PRMGs by R and Perl software. Hub genes were filtered out by univariate Cox analysis and least absolute shrinkage and selection operator Cox analysis. We used functional enrichment analysis methods, such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, to identify involved signaling pathways of PRMGs. The nomogram predicted overall survival (OS). Calibration traces were used to evaluate the consistency between the actual and the predicted survival rate. Finally, a prognostic model was constructed based on six metabolic genes (NAT2, XDH, GPX3, AKR1C4, SPHK1, and ADCY5), and the risk score was an independent prognostic prognosticator. Genetic expression and risk score were significantly correlated with clinicopathologic characteristics of CRC. A nomogram based on the clinicopathological feature of CRC and risk score accurately predicted the OS of individual CRC cancer patients. We also validated the results in the independent colorectal cancer cohorts GSE39582 and GSE87211. Our study demonstrates that the risk score is an independent prognostic biomarker and is closely correlated with the malignant clinicopathological characteristics of CRC patients. We also determined some metabolic genes associated with the survival and clinical stage of CRC as potential biomarkers for CRC diagnosis and treatment.

Clinical and Economic Impact of Tailoring Screening to Predicted Colorectal Cancer Risk: A Decision Analytic Modeling Study

BACKGROUND

Global increases in colorectal cancer risk have spurred debate about optimal use of screening resources. We explored the potential clinical and economic impact of colorectal cancer screening tailored to predicted colorectal cancer risk.

METHODS

We compared screening tailored to predicted risk versus uniform screening in a validated decision analytic model, considering the average risk population's actual colorectal cancer risk distribution, and a risk-prediction tool's discriminatory ability and cost. Low, moderate, and high risk tiers were identified as colorectal cancer risk after age 50 years of ≤3%, >3 to <12%, and ≥12%, respectively, based on threshold analyses with willingness-to-pay <$50,000/quality-adjusted life-year (QALY) gained. Tailored colonoscopy (once at age 60 years for low risk, every 10 years for moderate risk, and every 5 years for high risk) was compared with colonoscopy every 10 years for all. Tailored fecal immunochemical testing (FIT)/colonoscopy (annual FIT for low and moderate risk, colonoscopy every 5 years for high risk) was compared with annual FIT for all.

RESULTS

Assuming no colorectal cancer risk misclassification or risk-prediction tool costs, tailored screening was preferred over uniform screening. Tailored colonoscopy was minimally less effective than uniform colonoscopy, but saved $90,200-$889,000/QALY; tailored FIT/colonoscopy yielded more QALYs/person than annual FIT at $10,600-$60,000/QALY gained. Relatively modest colorectal cancer risk misclassification rates or risk-prediction tool costs resulted in uniform screening as the preferred approach.

CONCLUSIONS

Current risk-prediction tools may not yet be accurate enough to optimize colorectal cancer screening.

IMPACT

Uniform screening is likely to be preferred over tailored screening if a risk-prediction tool is associated with even modest misclassification rates or costs.

Advanced neoplasia in Veterans at screening colonoscopy using the National Cancer Institute Risk Assessment Tool

BACKGROUND

Adapting screening strategy to colorectal cancer (CRC) risk may improve efficiency for all stakeholders however limited tools for such risk stratification exist. Colorectal cancers usually evolve from advanced neoplasms that are present for years. We applied the National Cancer Institute (NCI) CRC Risk Assessment Tool, which calculates future risk of CRC, to determine whether it could be used to predict current advanced neoplasia (AN) in a veteran cohort undergoing a baseline screening colonoscopy.

METHODS

This was a prospective assessment of the relationship between future CRC risk predicted by the NCI tool, and the presence of AN at screening colonoscopy. Family, medical, dietary and physical activity histories were collected at the time of screening colonoscopy and used to calculate absolute CRC risk at 5, 10 and 20 years. Discriminatory accuracy was assessed.

RESULTS

Of 3121 veterans undergoing screening colonoscopy, 94% had complete data available to calculate risk (N = 2934, median age 63 years, 100% men, and 15% minorities). Prevalence of AN at baseline screening colonoscopy was 11 % (N = 313). For tertiles of estimated absolute CRC risk at 5 years, AN prevalences were 6.54% (95% CI, 4.99, 8.09), 11.26% (95% CI, 9.28-13.24), and 14.21% (95% CI, 12.02-16.40). For tertiles of estimated risk at 10 years, the prevalences were 6.34% (95% CI, 4.81-7.87), 11.25% (95% CI, 9.27-13.23), and 14.42% (95% CI, 12.22-16.62). For tertiles of estimated absolute CRC risk at 20 years, current AN prevalences were 7.54% (95% CI, 5.75-9.33), 10.53% (95% CI, 8.45-12.61), and 12.44% (95% CI, 10.2-14.68). The area under the curve for predicting current AN was 0.60 (95% CI; 0.57-0.63, p < 0.0001) at 5 years, 0.60 (95% CI, 0.57-0.63, p < 0.0001) at 10 years and 0.58 (95% CI, 0.54-0.61, p < 0.0001) at 20 years.

CONCLUSION

The NCI tool had modest discriminatory function for estimating the presence of current advanced neoplasia in veterans undergoing a first screening colonoscopy. These findings are comparable to other clinically utilized cancer risk prediction models and may be used to inform the benefit-risk assessment of screening, particularly for patients with competing comorbidities and lower risk, for whom a non-invasive screening approach is preferred.

Development of a colorectal cancer diagnostic model and dietary risk assessment through gut microbiome analysis

Colorectal cancer (CRC) is the third most common form of cancer and poses a critical public health threat due to the global spread of westernized diets high in meat, cholesterol, and fat. Although the link between diet and colorectal cancer has been well established, the mediating role of the gut microbiota remains elusive. In this study, we sought to elucidate the connection between the gut microbiota, diet, and CRC through metagenomic analysis of bacteria isolated from the stool of CRC (n = 89) and healthy (n = 161) subjects. This analysis yielded a dozen genera that were significantly altered in CRC patients, including increased Bacteroides, Fusobacterium, Dorea, and Porphyromonas prevalence and diminished Pseudomonas, Prevotella, Acinetobacter, and Catenibacterium carriage. Based on these altered genera, we developed two novel CRC diagnostic models through stepwise selection and a simplified model using two increased and two decreased genera. As both models yielded strong AUC values above 0.8, the simplified model was applied to assess diet-based CRC risk in mice. Mice fed a westernized high-fat diet (HFD) showed greater CRC risk than mice fed a regular chow diet. Furthermore, we found that nonglutinous rice, glutinous rice, and sorghum consumption reduced CRC risk in HFD-fed mice. Collectively, these findings support the critical mediating role of the gut microbiota in diet-induced CRC risk as well as the potential of dietary grain intake to reduce microbiota-associated CRC risk. Further study is required to validate the diagnostic prediction models developed in this study as well as the preventive potential of grain consumption to reduce CRC risk.

Similarities and differences in health-related behavior clustering among older adults in Eastern and Western countries: A latent class analysis of global aging cohorts

AIM

To quantify variations in health-related behaviors (HRB) clustering of older adults in Western and Eastern countries.

METHODS

Using six aging cohorts from the USA, England, Europe, Japan, Korea and China, latent class analysis was applied to access the clustering of smoking, alcohol consumption, physical activity and social activity.

RESULTS

A total of 104 552 participants (55% women) aged ≥50 years in 2010 were included. Despite a different number of clusters identified, three consistent cluster profiles emerged: "Multiple-HRB" (ex-/never smoking, moderate drinking, frequent physical and social activity); "Inactives" (socially and physically inactive without other risk behaviors); and "(ex-)Smokers with Risk Behaviors". Sex and cohort variations were shown. For men in Western cohorts, "Multiple-HRB" was the predominant cluster, whereas their Asian counterparts were more likely to be members of the "Smokers with risk behavior" and "Inactives" clusters. Most women, particularly those in Asian cohorts, were never smokers and non-drinkers, and most of them belonged to the socially "Inactives" cluster.

CONCLUSIONS

We provide a person-centered understanding of HRB clustering of older adults over selected countries by sex, informing tailored health promotion for the target population. Geriatr Gerontol Int 2019; 19: 930-937.

Prediction of colorectal cancer risk among adults in a lower middle-income country

Background

Globally, colorectal cancer (CRC) is ranked as the third most common cancer in men and the second in women. Use of a simple, validated risk prediction tool will offer a low-cost mechanism to identify the high-risk individuals for CRC. This will increase efficient use of limited resources and early identification of patients. The aim of our study was to develop and validate a risk prediction model for developing CRC for Sri Lankan adults.

Methods

The risk predictors were based on the risk factors identified through a logistic regression model along with expert opinion. A case control design utilizing 65 CRC new cases and 65 hospital controls aged 30 years or more was used to assess the criterion validity and reliability of the model. The information was obtained using an interviewer administered questionnaire based on the risk prediction model.

Results

The developed model consisted of eight predictors with an area under the curve (AUC) of 0.849 (95% CI: 0.8 to 0.9, P<0.001). It has a sensitivity of 76.9%, specificity of 83.1%, positive predictive value (PPV) of 82.0%, negative predictive value (NPV) of 79.3%. Positive and negative likelihood ratios are 4.6 and 0.3. Test re-test reliability revealed a Kappa coefficient of 0.88.

Conclusions

The model developed to predict the risk of CRC among adults aged 30 years and above was proven to be valid and reliable and it is an effective tool to be used as the first step to identify the high-risk population who should be referred for colonoscopy examination.

Comparison of Questionnaire-Based Breast Cancer Prediction Models in the Nurses' Health Study

BACKGROUND

The Gail model and the model developed by Tyrer and Cuzick are two questionnaire-based approaches with demonstrated ability to predict development of breast cancer in a general population.

METHODS

We compared calibration, discrimination, and net reclassification of these models, using data from questionnaires sent every 2 years to 76,922 participants in the Nurses' Health Study between 1980 and 2006, with 4,384 incident invasive breast cancers identified by 2008 (median follow-up, 24 years; range, 1-28 years). In a random one third sample of women, we also compared the performance of these models with predictions from the Rosner-Colditz model estimated from the remaining participants.

RESULTS

Both the Gail and Tyrer-Cuzick models showed evidence of miscalibration (Hosmer-Lemeshow P < 0.001 for each) with notable (P < 0.01) overprediction in higher-risk women (2-year risk above about 1%) and underprediction in lower-risk women (risk below about 0.25%). The Tyrer-Cuzick model had slightly higher C-statistics both overall (P < 0.001) and in age-specific comparisons than the Gail model (overall C, 0.63 for Tyrer-Cuzick vs. 0.61 for the Gail model). Evaluation of net reclassification did not favor either model. In the one third sample, the Rosner-Colditz model had better calibration and discrimination than the other two models. All models had C-statistics <0.60 among women ages ≥70 years.

CONCLUSIONS

Both the Gail and Tyrer-Cuzick models had some ability to discriminate breast cancer cases and noncases, but have limitations in their model fit.

IMPACT

Refinements may be needed to questionnaire-based approaches to predict breast cancer in older and higher-risk women.

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

OBJECTIVE

To systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts.

DESIGN

Models were identified through an update of a published systematic review and validated in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted probability).

RESULTS

The systematic review and its update identified 16 models from 8 publications (8 colorectal, 5 colon and 3 rectal). The number of participants included in each model validation ranged from 41 587 to 396 515, and the number of cases ranged from 115 to 1781. Eligible and ineligible participants across the models were largely comparable. Calibration of the models, where assessable, was very good and further improved by recalibration. The C-statistics of the models were largely similar between validation cohorts with the highest values achieved being 0.70 (95% CI 0.68 to 0.72) in the UK Biobank and 0.71 (95% CI 0.67 to 0.74) in EPIC.

CONCLUSION

Several of these non-invasive models exhibited good calibration and discrimination within both external validation populations and are therefore potentially suitable candidates for the facilitation of risk stratification in population-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained.

A prognostic index for long-term outcome after successful acute phase cognitive therapy and interpersonal psychotherapy for major depressive disorder

BACKGROUND

Major depressive disorder (MDD) has a highly recurrent nature. After successful treatment, it is important to identify individuals who are at risk of an unfavorable long-term course. Despite extensive research, there is no consensus yet on the clinically relevant predictors of long-term outcome in MDD, and no prediction models are implemented in clinical practice. The aim of this study was to create a prognostic index (PI) to estimate long-term depression severity after successful and high quality acute treatment for MDD.

METHODS

Data come from responders to cognitive therapy (CT) and interpersonal psychotherapy (IPT) in a randomized clinical trial (n = 85; CT = 45, IPT = 40). Primary outcome was depression severity, assessed with the Beck Depression Inventory II, measured throughout a 17-month follow-up phase. We examined 29 variables as potential predictors, using a model-based recursive partitioning method and bootstrap resampling in conjunction with backwards elimination. The selected predictors were combined into a PI. Individual PI scores were estimated using a cross-validation approach.

RESULTS

A total of three post-treatment predictors were identified: depression severity, hopelessness, and self-esteem. Cross-validated PI scores evidenced a strong correlation (r = 0.60) with follow-up depression severity.

CONCLUSION

Long-term predictions of MDD are multifactorial, involving a combination of variables that each has a small prognostic effect. If replicated and validated, the PI can be implemented to predict follow-up depression severity for each individual after acute treatment response, and to personalize long-term treatment strategies.

A scoring model for predicting advanced colorectal neoplasia in a screened population of asymptomatic Japanese individuals

BACKGROUND

Risk stratification of screened populations could help improve colorectal cancer (CRC) screening. Use of the modified Asia-Pacific Colorectal Screening (APCS) score has been proposed in the Asia-Pacific region. This study was performed to build a new useful scoring model for CRC screening.

METHODS

Data were reviewed from 5218 asymptomatic Japanese individuals who underwent their first screening colonoscopy. Multivariate logistic regression was used to investigate risk factors for advanced colorectal neoplasia (ACN), and a new scoring model for the prediction of ACN was developed based on the results. The discriminatory capability of the new model and the modified APCS score were assessed and compared. Internal validation was also performed.

RESULTS

ACN was detected in 225 participants. An 8-point scoring model for the prediction of ACN was developed using five independent risk factors for ACN (male sex, higher age, presence of two or more first-degree relatives with CRC, body mass index of > 22.5 kg/m², and smoking history of > 18.5 pack-years). The prevalence of ACN was 1.6% (34/2172), 5.3% (127/2419), and 10.2% (64/627) in participants with scores of < 3, ≥ 3 to < 5, and ≥ 5, respectively. The c-statistic of the scoring model was 0.70 (95% confidence interval, 0.67-0.73) in both the development and internal validation sets, and this value was higher than that of the modified APCS score [0.68 (95% confidence interval, 0.65-0.71), P = 0.03].

CONCLUSIONS

We built a new simple scoring model for prediction of ACN in a Japanese population that could stratify the screened population into low-, moderate-, and high-risk groups.

Predicting the presence of adenomatous polyps during colonoscopy with National Cancer Institute Colorectal Cancer Risk-Assessment Tool

AIM

To evaluate the National Cancer Institute (NCI) Colorectal Cancer (CRC) Risk Assessment Tool as a predictor for the presence of adenomatous polyps (AP) found during screening or surveillance colonoscopy.

METHODS

This is a retrospective single center observational study. We collected data of adenomatous polyps in each colonoscopy and then evaluated the lifetime CRC risk. We calculated the AP prevalence across risk score quintiles, odds ratios of the prevalence of AP across risk score quintiles, area under curves (AUCs) and Youden’s indexes to assess the optimal risk score cut off value for AP prevalence status.

RESULTS

The prevalence of AP gradually increased throughout the five risk score quintiles: i.e., 27.63% in the first and 51.35% in the fifth quintile. The odd ratios of AP prevalence in the fifth quintile compared to the first and second quintile were 2.76 [confidence interval (CI): 1.71-4.47] and 2.09 (CI: 1.32-3.30). The AUC for all patients was 0.62 (CI: 0.58-0.66). Youden’s Index indicated the optimal risk score cutoff value discriminating AP prevalence status was 3.60.

CONCLUSION

Patients with the higher NCI risk score have higher risk of AP and subsequent CRC; therefore, measures to increase the effectiveness of CRC detection in these patients include longer withdrawal time, early surveillance colonoscopy, and choosing flexible colonoscopy over other CRC screening modalities.

Youth at-risk for serious mental illness: methods of the PROCAN study

BACKGROUND

Most mental disorders begin in adolescence; however, there are gaps in our understanding of youth mental health. Clinical and policy gaps arise from our current inability to predict, from amongst all youth who experience mild behavioural disturbances, who will go on to develop a mental illness, what that illness will be, and what can be done to change its course and prevent its worsening to a serious mental illness (SMI). There are also gaps in our understanding of how known risk factors set off neurobiological changes that may play a role in determining who will develop a SMI. Project goals are (i) to identify youth at different stages of risk of SMI so that intervention can begin as soon as possible and (ii) to understand the triggers of these mental illnesses.

METHOD

This 2-site longitudinal study will recruit 240 youth, ages 12-25, who are at different stages of risk for developing a SMI. The sample includes (a) healthy individuals, (b) symptom-free individuals who have a first-degree relative with a SMI, (c) youth who are experiencing distress and may have mild symptoms of anxiety or depression, and (d) youth who are already demonstrating attenuated symptoms of SMI such as bipolar disorder or psychosis. We will assess, every 6 months for one year, a wide range of clinical and psychosocial factors to determine which factors can be used to predict key outcomes. We will also assess neuroimaging and peripheral markers. We will develop and validate a prediction algorithm that includes demographic, clinical and psychosocial predictors. We will also determine if adding biological markers to our algorithm improves prediction.

DISCUSSION

Outcomes from this study include an improved clinical staging model for SMI and prediction algorithms that can be used by health care providers as decision-support tools in their practices. Secondly, we may have a greater understanding of clinical, social and cognitive factors associated with the clinical stages of development of a SMI, as well as new insights from neuroimaging and later neurochemical biomarker studies regarding predisposition to SMI development and progression through the clinical stages of illness.

External validation of risk prediction models for incident colorectal cancer using UK Biobank

BACKGROUND

This study aimed to compare and externally validate risk scores developed to predict incident colorectal cancer (CRC) that include variables routinely available or easily obtainable via self-completed questionnaire.

METHODS

External validation of fourteen risk models from a previous systematic review in 373 112 men and women within the UK Biobank cohort with 5-year follow-up, no prior history of CRC and data for incidence of CRC through linkage to national cancer registries.

RESULTS

There were 1719 (0.46%) cases of incident CRC. The performance of the risk models varied substantially. In men, the QCancer10 model and models by Tao, Driver and Ma all had an area under the receiver operating characteristic curve (AUC) between 0.67 and 0.70. Discrimination was lower in women: the QCancer10, Wells, Tao, Guesmi and Ma models were the best performing with AUCs between 0.63 and 0.66. Assessment of calibration was possible for six models in men and women. All would require country-specific recalibration if estimates of absolute risks were to be given to individuals.

CONCLUSIONS

Several risk models based on easily obtainable data have relatively good discrimination in a UK population. Modelling studies are now required to estimate the potential health benefits and cost-effectiveness of implementing stratified risk-based CRC screening.

A modified colorectal screening score for prediction of advanced neoplasia: A prospective study of 5744 subjects

BACKGROUND AND AIM

We validated a modified risk algorithm based on the Asia-Pacific Colorectal Screening (APCS) score that included body mass index (BMI) for prediction of advanced neoplasia.

METHODS

Among 5744 Chinese asymptomatic screening participants undergoing a colonoscopy in Hong Kong from 2008 to 2012, a random sample of 3829 participants acted as the derivation cohort. The odds ratios for significant risk factors identified by binary logistic regression analysis were used to build a scoring system ranging from 0 to 6, divided into "average risk" (AR): 0; "moderate risk" (MR): 1-2; and "high risk" (HR): 3-6. The other 1915 subjects formed a validation cohort, and the performance of the score was assessed.

RESULTS

The prevalence of advanced neoplasia in the derivation and validation cohorts was 5.4% and 6.0%, respectively (P = 0.395). Old age, male gender, family history of colorectal cancer, smoking, and BMI were significant predictors in multivariate regression analysis. A BMI cut-off at > 23 kg/m² had better predictive capability and lower number needed to screen than that of > 25 kg/m² . Utilizing the score developed, 8.4%, 57.4%, and 34.2% in the validation cohort were categorized as AR, MR, and HR, respectively. The corresponding prevalence of advanced neoplasia was 3.8%, 4.3%, and 9.3%. Subjects in the HR group had 2.48-fold increased prevalence of advanced neoplasia than the AR group. The c-statistics of the modified score had better discriminatory capability than that using predictors of APCS alone (c-statistics = 0.65 vs 0.60).

CONCLUSIONS

Incorporating BMI into the predictors of APCS score was found to improve risk prediction of advanced neoplasia and reduce colonoscopy resources.

A Bayesian scoring rule on clustered event data for familial risk assessment - An example from colorectal cancer screening

Colorectal cancer screening is well established. The identification of high risk populations is the key to implement effective risk-adjusted screening. Good statistical approaches for risk prediction do not exist. The family's colorectal cancer history is used for identification of high risk families and usually assessed by a questionnaire. This paper introduces a prediction algorithm to designate a family for colorectal cancer risk and discusses its statistical properties. The new algorithm uses Bayesian reasoning and a detailed family history illustrated by a pedigree and a Lexis diagram. The algorithm is able to integrate different hereditary mechanisms that define complex latent class or random factor structures. They are generic and do not reflect specific genetic models. This is comparable to strategies in complex segregation analysis. Furthermore, the algorithm can integrate different statistical penetrance models for right censored event data. Computational challenges related to the handling of the likelihood are discussed. Simulation studies assess the predictive quality of the new algorithm in terms of ROC curves and corresponding AUCs. The algorithm is applied to data of a recent study on familial colorectal cancer risk. Its predictive performance is compared to that of a questionnaire currently used in screening for familial colorectal cancer. The results of the proposed algorithm are robust against different inheritance models. Using the simplest hereditary mechanism, the simulation study provides evidence that the algorithm improves detection of families with high cancer risk in comparison to the currently used questionnaire. The applicability of the algorithm goes beyond the field of colorectal cancer.

Prediction of Ischemic Heart Disease and Stroke in Survivors of Childhood Cancer

Purpose We aimed to predict individual risk of ischemic heart disease and stroke in 5-year survivors of childhood cancer. Patients and Methods Participants in the Childhood Cancer Survivor Study (CCSS; n = 13,060) were observed through age 50 years for the development of ischemic heart disease and stroke. Siblings (n = 4,023) established the baseline population risk. Piecewise exponential models with backward selection estimated the relationships between potential predictors and each outcome. The St Jude Lifetime Cohort Study (n = 1,842) and the Emma Children's Hospital cohort (n = 1,362) were used to validate the CCSS models. Results Ischemic heart disease and stroke occurred in 265 and 295 CCSS participants, respectively. Risk scores based on a standard prediction model that included sex, chemotherapy, and radiotherapy (cranial, neck, and chest) exposures achieved an area under the curve and concordance statistic of 0.70 and 0.70 for ischemic heart disease and 0.63 and 0.66 for stroke, respectively. Validation cohort area under the curve and concordance statistics ranged from 0.66 to 0.67 for ischemic heart disease and 0.68 to 0.72 for stroke. Risk scores were collapsed to form statistically distinct low-, moderate-, and high-risk groups. The cumulative incidences at age 50 years among CCSS low-risk groups were < 5%, compared with approximately 20% for high-risk groups ( P < .001); cumulative incidence was only 1% for siblings ( P < .001 v low-risk survivors). Conclusion Information available to clinicians soon after completion of childhood cancer therapy can predict individual risk for subsequent ischemic heart disease and stroke with reasonable accuracy and discrimination through age 50 years. These models provide a framework on which to base future screening strategies and interventions.

Screening of colorectal cancer: present and future

INTRODUCTION

Colorectal cancer (CRC) is the third most common cancer in males and second in females, and the fourth most common cause of cancer death worldwide. Currently, about 60-70% of diagnosed cases in symptomatic patients are detected at an advanced stage of disease. Earlier stage detection through the use of screening strategies would allow for better outcomes in terms of reducing the disease burden. Areas covered: The aim of this paper is to review the current published evidence from literature which assesses the performance and effectiveness of different screening tests for the early detection of CRC. Expert commentary: Adequate screening strategies can reduce CRC incidence and mortality. In the last few decades, several tests have been proposed for CRC screening. To date, there is still insufficient evidence to identify which approach is definitively superior, and no screening strategy for CRC can therefore be defined as universally ideal. The best strategy would be the one that can be economically viable and to which the patient can adhere best to over time. The latest guidelines suggest colonoscopy every 10 years or annual fecal immuno-chemical test (FIT) for people with normal risk, while for individuals with high risk or hereditary syndromes specific recommendations are provided.

Individualized Approach to Cancer Screening in Older Adults

The primary goal of cancer screening is early detection of cancer to reduce cancer-specific mortality and morbidity. The benefits of screening in older adults are uncertain due to paucity of evidence. Extrapolating data from younger populations, evidence suggests that the benefit occurs years later from the time of initial screening and therefore may not be applicable in those older adults with limited life expectancy. Contrast this with the harms of screening, which are more immediate and increase with age and comorbidities. An individualized approach to cancer screening takes these factors into consideration, allowing for thoughtful decision making for older adults.

A prediction model for advanced colorectal neoplasia in an asymptomatic screening population

Background

An electronic medical record (EMR) database of a large unselected population who received screening colonoscopies may minimize sampling error and represent real-world estimates of risk for screening target lesions of advanced colorectal neoplasia (CRN). Our aim was to develop and validate a prediction model for assessing the probability of advanced CRN using a clinical data warehouse.

Methods

A total of 49,450 screenees underwent their first colonoscopy as part of a health check-up from 2002 to 2012 at Samsung Medical Center, and the dataset was constructed by means of natural language processing from the computerized EMR system. The screenees were randomized into training and validation sets. The prediction model was developed using logistic regression. The model performance was validated and compared with existing models using area under receiver operating curve (AUC) analysis.

Results

In the training set, age, gender, smoking duration, drinking frequency, and aspirin use were identified as independent predictors for advanced CRN (adjusted P < .01). The developed model had good discrimination (AUC = 0.726) and was internally validated (AUC = 0.713). The high-risk group had a 3.7-fold increased risk of advanced CRN compared to the low-risk group (1.1% vs. 4.0%, P < .001). The discrimination performance of the present model for high-risk patients with advanced CRN was better than that of the Asia-Pacific Colorectal Screening score (AUC = 0.678, P < .001) and Schroy’s CAN index (AUC = 0.672, P < .001).

Conclusion

The present 5-item risk model can be calculated readily using a simple questionnaire and can identify the low- and high-risk groups of advanced CRN at the first screening colonoscopy. This model may increase colorectal cancer risk awareness and assist healthcare providers in encouraging the high-risk group to undergo a colonoscopy.

Multiple gene-specific DNA methylation in blood leukocytes and colorectal cancer risk: a case-control study in China

The relationship between gene-specific DNA methylation in peripheral blood leukocytes and colorectal cancer (CRC) susceptibility is unclear. In this case-control study, the methylation status of a panel of 10 CRC-related genes in 428 CRC cases and 428 cancer-free controls were detected with methylation-sensitive high-resolution melting analysis. We calculated a weighted methylation risk score (MRS) that comprehensively combined the methylation status of the panel of 10 genes and found that the MRS_10 was significantly associated with CRC risk. Compared with MRS-Low group, MRS-High group and MRS-Medium group exhibited a 6.51-fold (95% CI, 3.77-11.27) and 3.85-fold (95% CI, 2.72-5.45) increased risk of CRC, respectively. Moreover, the CRC risk increased with increasing MRS_10 (P_trend < 0.0001). Stratified analyses demonstrated that the significant association retained in both men and women, younger and older, and normal weight or underweight and overweight or obese subjects. The area under the receiver operating characteristic curves for the MRS_10 model was 69.04% (95% CI, 65.57-72.66%) and the combined EF and MRS_10 model yielded an AUC of 79.12% (95% CI, 76.22-82.15%). Together, the panel of 10 gene-specific DNA methylation in leukocytes was strongly associated with the risk of CRC and might be a useful marker of susceptibility for CRC.