Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach.

ABSTRACT

BACKGROUND: Accurate estimation of the long-term risk of recurrence in patients with non-metastatic colorectal cancer (CRC) is crucial for clinical management. Histology-based deep learning is expected to provide more abundant information for risk stratification. METHODS: We developed and validated a weakly supervised deep-learning model for predicting 5-year relapse-free survival (RFS) to stratify patients with different risks based on histological images from three hospitals of 614 cases with non-metastatic CRC. A deep prognostic factor (DL-RRS) was established to stratify patients into high and low-risk group. The areas under the curve (AUCs) were calculated to evaluate the performances of models. RESULTS: Our proposed model achieves the AUCs of 0.833 (95% CI 0.736-0.905) and 0.715 (95% CI 0.647-0.776) on validation cohort and external test cohort, respectively. The 5-year RFS rate was 45.7% for high DL-RRS patients, and 82.5% for low DL-RRS patients respectively in the external test cohort (HR 3.89, 95% CI 2.51-6.03, P < 0.001). Adjuvant chemotherapy was associated with improved RFS in Stage II patients with high DL-RRS (HR 0.15, 95% CI 0.06-0.38, P < 0.001). CONCLUSIONS: DL-RRS has a good predictive performance of 5-year recurrence risk in CRC, and will better serve the clinical decision-making.