The study investigated the possibility of using deep learning to analyze histopathological images of patients with stage II colorectal cancer. The research team developed a system called SurvFinder that automatically identifies risk biomarkers from tissue samples stained with hematoxylin and eosin. The study included 6,950 histopathological images from 1,604 patients from four independent centers in China who were followed for at least 24 months. The main finding was that tertiary lymphoid structures (TLS) are critical prognostic features, with their location at the tumor periphery and degree of maturity significantly affecting prognosis. The SurvFinder system achieved exceptional predictive accuracy with AUROC values ranging from 0.805 to 0.871 across individual datasets, outperforming traditional clinical prognostic parameters. The results suggest that deep learning-based histopathological analysis could aid in automated risk stratification and personalization of adjuvant chemotherapy decisions in stage II patients. The main limitation of the study is its retrospective nature without prospective validation and real-world clinical deployment.