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Preoperative risk stratification for pathological upgrading in colorectal polyps using explainable machine learning:

Bo Yang¹, Chang Zhang^2,3, Mingsu Gong¹

¹Department of Gastroenterology and Hepatology, Guizhou Aerospace Hospital, Zunyi, China.

Frontiers in Public Health

|June 8, 2026

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This summary is machine-generated.

Pathological upgrading in colorectal polyps is common. A new machine learning model accurately predicts this risk using key features like lesion location, aiding early detection and treatment for colorectal cancer prevention.

Area of Science:

Oncology
Gastroenterology
Machine Learning in Medicine

Background:

Pathological upgrading of colorectal polyps, where the resected specimen shows a higher grade than the biopsy, leads to underdiagnosis and suboptimal treatment.
Colorectal cancer (CRC) poses a significant global health burden, necessitating improved preoperative risk stratification for polyps.
Accurate risk assessment is crucial for optimizing CRC screening, prevention strategies, and resource allocation.

Purpose of the Study:

To develop and validate a machine learning model for predicting pathological upgrading in colorectal polyps.
To identify key clinical and endoscopic predictors of pathological upgrading.
To create an interpretable and clinically applicable tool for preoperative risk assessment.

Main Methods:

Keywords:

Explainable artificial intelligence (SHAP)colorectal cancer prevention colorectal polyps machine learning risk stratification

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A retrospective study included 593 patients undergoing endoscopic biopsy and resection of colorectal polyps.
LASSO regression and Boruta algorithm were used for feature selection.
Six machine learning models (RF, CART, NNet, LR, GBM, XGBoost) were developed and evaluated using cross-validation, with performance metrics including AUC, accuracy, sensitivity, specificity, MCC, and Brier score. SHapley Additive exPlanations (SHAP) were used for model interpretation.

Main Results:

150 patients (25.3%) experienced pathological upgrading.
Key predictors identified were maximum tumor diameter, surface color, erosion, villous structure, and lesion location.
The XGBoost model demonstrated superior performance (AUC 0.890 training, 0.863 test) and was well-calibrated. SHAP analysis highlighted lesion location (especially rectal) as the most influential factor.

Conclusions:

A machine learning model effectively predicts pathological upgrading in colorectal polyps, offering interpretable insights via SHAP analysis.
The developed model and accompanying web-based calculator can aid clinicians in preoperative risk assessment and decision-making.
Improved risk stratification has the potential to enhance early detection, refine endoscopic management, and optimize CRC prevention efforts.