A Machine-Learning Approach to Predicting Adenoma Detection and Surveillance Impact of Deeper Sections in Colorectal
Context.—:
In the routine practice of pathology, cutting deeper levels into paraffin blocks of colorectal polypectomy specimens that do not show a lesion on initial hematoxylin-eosin sections is common. However, this practice lacks standardization, and data are limited on the predictability of adenoma detection and the impact on surveillance intervals and resource use.
Objective.—:
To develop machine learning models that predict which polyps and encounters are most likely to reveal clinically significant lesions on deeper levels and influence patient surveillance intervals.
Design.—:
We performed a retrospective study of 94 888 patients (145 405 colonoscopies; 392 130 polyps) from 2007 to 2024. Polyp characteristics, deeper-level requests, and clinical/endoscopic features were analyzed. Machine learning models were developed to predict (1) adenoma detection in single polyps with deeper levels and (2) impact on surveillance intervals per encounter.
Results.—:
Deeper levels were requested in 26 067 jars (11%) and 21 232 encounters (15%), revealing adenomas in 7946 single polyps examined (51%). Predictive models showed high performance: logistic regression for single-polyp conversion (area under the receiver operating characteristic curve 0.88, accuracy 81%) and gradient boosting classifier for surveillance interval impact (area under the receiver operating characteristic curve 0.90, accuracy 83%). Important predictors included polyp size, location, total polyps, and artificial intelligence-assisted colonoscopy.
Conclusions.—:
Deeper-level sectioning contributes significantly to adenoma detection and influences surveillance intervals. Machine learning provides a practical framework to optimize deeper-level requests, enhancing diagnostic precision and resource allocation.
