Clinically meaningful risk factors for recurrence in T1 colorectal cancer treated with endoscopic resection alone identified by unsupervised machine learning: a multicenter study.

BACKGROUND

Identifying patients at high risk for recurrence after endoscopic resection of T1 colorectal cancer (CRC) remains challenging. This study aimed to identify recurrence risk subtypes and develop an interpretable risk stratification framework.

METHODS

This retrospective study analyzed 1123 patients with T1 CRC treated with endoscopic resection alone across 26 Japanese institutions (July 2009-December 2016). Patients were divided into development (68%) and evaluation (32%) cohorts based on institutional stratification. K-means clustering was applied to clinicopathologic variables to identify recurrence risk subtypes. A decision tree classifier was subsequently developed to generate transparent risk stratification rules.

RESULTS

Three distinct subtypes were identified in the development cohort. Subtype 1 exhibited a numerically higher recurrence rate (5.4%) than Subtype 2 (0.9%) and Subtype 3 (1.4%). Although subtypes 2 and 3 showed comparable recurrence rates, they were clearly differentiated by morphology (flat vs. polypoid). In the evaluation cohort, Subtype 1 continued to show a numerically higher recurrence (4.8%) compared with subtypes 2 (1.6%) and 3 (1.1%). The decision tree model stratified recurrence risk hierarchically: submucosal invasion <1000 μm indicated low risk, whereas invasion ≥1000 μm required morphologic assessment, with polypoid lesions classified as high risk and flat lesions further stratified using a 2000-μm threshold.

CONCLUSIONS

Three clinically distinct recurrence risk subtypes were identified in T1 CRC following endoscopic resection, suggesting that morphologic subclassification of T1b lesions may refine stratification beyond conventional depth-based criteria. The decision framework offers a preliminary exploratory basis for recurrence risk assessment in this population.