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Predicting High-Risk Colorectal Polyps in African Americans Using Pre-colonoscopy Clinical Features: Machine Learning

Basheer Qolomany¹, Mrinalini Deverapall², Adeyinka Laiyemo²

¹Departments of Internal Medicine, Pathology, and Biochemistry, and Cancer Center, Howard University College of Medicine, Washington, D.C., 20059, USA. Basheer.Qolomany@Howard.edu.

Digestive Diseases and Sciences

|June 23, 2026

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This summary is machine-generated.

Predicting high-risk colorectal polyps using noninvasive, pre-colonoscopy data is feasible but shows limited generalizability. Machine learning models identified key demographic and clinical factors, highlighting potential for risk stratification, especially in diverse populations.

Area of Science:

Colorectal cancer research
Machine learning in healthcare
Epidemiology of gastrointestinal diseases

Background:

Traditional risk stratification for advanced colorectal polyps relies on colonoscopy and pathology.
Noninvasive, pre-colonoscopy features offer potential for enhanced clinical decision-making and equitable risk assessment.
Identifying high-risk patients pre-colonoscopy can optimize resource allocation and reduce unnecessary procedures.

Purpose of the Study:

To develop and externally validate machine learning models for predicting high-risk colorectal polyps (HRP).
To utilize only noninvasive, pre-colonoscopy demographic, clinical, and behavioral features.
To assess model performance in a diverse, urban cohort, predominantly African American.

Main Methods:

Keywords:

Adenomatous polyps Colonoscopy Colorectal cancer screening Machine learning Risk stratification

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A retrospective cohort study involving 4,681 patients for internal validation and 1,562 for external validation.

Development and comparison of multiple machine learning models (neural networks, random forest, SVM, Naïve Bayes, logistic regression, decision trees, KNN, XGBoost).

High-risk polyps defined by histology (villous/tubullovillous adenomas, high-grade dysplasia), size (≥10 mm), or number (≥3). Performance evaluated using ROC-AUC, PR-AUC, accuracy, precision, recall, and F1 score; interpretability via SHAP.

Main Results:

Overall predictive performance using noninvasive features was moderate.
Neural networks showed highest internal performance (ROC-AUC 0.78) but poorer external validation (ROC-AUC 0.67).
Simpler models (Naïve Bayes, SVM, XGBoost) had lower internal performance but more stable external generalization (ROC-AUC ~0.52-0.63). Key predictors included age, smoking status, sex, occupation, race, indication for colonoscopy, and family history.

Conclusions:

Prediction of high-risk colorectal polyps using routine pre-colonoscopy data is feasible but exhibits limited generalizability.
Findings underscore the clinical potential and limitations of pre-procedural risk modeling, particularly in diverse, underserved populations.
Integration of additional data modalities may be necessary for robust and equitable prediction tools.