Enhanced risk stratification for stage II colorectal cancer using deep learning-based CT classifier and pathological markers to optimize adjuvant therapy decision

Abstract

Background: Current risk stratification for stage II colorectal cancer (CRC) has limited accuracy in identifying patients who would benefit from adjuvant chemotherapy, leading to potential overtreatment or undertreatment. We aimed to develop a more precise risk stratification system by integrating artificial intelligence-based imaging analysis with pathological markers.

Patients and methods: We analyzed 2992 stage II CRC patients from 12 centers. A deep learning classifier (Swin Transformer Assisted Risk-stratification for CRC, STAR-CRC) was developed using multi-planar computed tomography (CT) images from 1587 patients (training : internal validation = 7 : 3) and validated in 1405 patients from eight independent centers, which stratified patients into low-, uncertain-, and high-risk groups. To further refine the uncertain-risk group, a composite score based on pathological markers (pT4 stage, number of lymph nodes sampled, perineural invasion, and lymphovascular invasion) was applied, forming the Intelligent Risk Integration System for stage II Colorectal Cancer (IRIS-CRC). IRIS-CRC was compared against the guideline-based risk stratification system (GRSS-CRC) for prediction performance and validated in the validation dataset.

Results: IRIS-CRC stratified patients into four prognostic groups with distinct 3-year disease-free survival rates (≥95%, 95%-75%, 75%-55%, ≤55%). Upon external validation, compared with GRSS-CRC, IRIS-CRC downstaged 27.1% of high-risk patients into the favorable group, and upstaged 6.5% of low-risk patients into the very poor prognosis group who might require more aggressive treatment. In the GRSS-CRC intermediate-risk group of the external validation dataset, IRIS-CRC reclassified 40.1% as favorable prognosis and 7.0% as very poor prognosis. IRIS-CRC's performance maintained generalizability in both chemotherapy and non-chemotherapy cohorts.

Conclusions: IRIS-CRC offers a more precise and personalized risk assessment than current guideline-based risk factors, potentially sparing low-risk patients from unnecessary adjuvant chemotherapy while identifying high-risk individuals for more aggressive treatment. This novel approach holds promise for improving clinical decision-making and outcomes in stage II CRC.

Keywords: colorectal cancer; computed tomography; deep learning; disease-free survival; prognosis.