Predicting High-Risk Colorectal Polyps in African Americans Using Pre-Colonoscopy Clinical Features: Machine Learning Model Development and Temporal Validation

2026-06-19

AI Summary

This study addresses the need for equitable and efficient colorectal cancer screening by developing and temporally validating machine learning models to predict high-risk colorectal polyps in African Americans using non-invasive clinical features collected prior to colonoscopy. Leveraging demographic, clinical, and behavioral variables, we present the first such models—implemented with XGBoost, random forest, and neural networks—in a predominantly African American cohort. Evaluated on 4,681 internal samples and validated on 1,562 external temporal samples, the models demonstrate robust performance in identifying individuals at elevated risk, thereby enabling more targeted colonoscopy referrals. This approach not only reduces unnecessary procedures but also supports data-driven precision screening strategies that enhance resource allocation and promote health equity.

Abstract

Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing interest in whether non-invasive features available prior to colonoscopy can help identify patients at higher risk. Such approaches may enhance clinical decision-making by prioritizing surveillance for individuals most likely to harbor high-risk polyps, when colonoscopy resources are limited while potentially reducing unnecessary procedures in lower-risk patients. Importantly, the use of non-invasive, pre-procedural information may also help promote more equitable access to risk stratification, particularly in settings where colonoscopy resources are limited or unevenly distributed. We aimed to develop and externally validate machine learning models to predict high-risk colorectal polyps using only non-invasive, pre-colonoscopy demographic, clinical, and behavioral features in a diverse, predominantly African American, urban cohort. We conducted a retrospective cohort study using demographic, lifestyle, and comorbidity data from patients who underwent colonoscopy at Howard University Hospital to develop and validate several machine learning models, including neural networks, random forest, support vector machines (SVM), Naive Bayes, logistic regression, decision trees, k-nearest neighbors (KNN), and XGBoost, for predicting high-risk colorectal polyps. High-risk polyps (HRP) were defined as villous or tubullovillous adenomas, high-grade dysplasia, polyps >= 10 mm in size, and/or the presence of >= 3 polyps per procedure; all other cases were classified as low-risk polyps (LRP). The dataset included 4,681 patients from 2015-2022 used for internal validation and 1,562 patients from 2023-2024 used for external validation.

Problem

• colorectal polyps
• risk stratification
• pre-colonoscopy
• African Americans
• machine learning

Innovation

• machine learning
• pre-colonoscopy prediction
• high-risk colorectal polyps
• temporal validation
• health equity