Multiview deep-learning-enabled histopathology for prognostic and therapeutic stratification in stage II colorectal cancer: A retrospective multicenter study.

backgroundApproximately 20% of patients with stage II colorectal cancer (CRC) experience tumor relapse despite standard surgical treatment. Histopathological analysis holds promise for postsurgical risk stratification and guiding adjuvant chemotherapy (ACT) decisions. The aim of this study was to use deep learning to extract explainable tissue biomarkers from whole-slide images. METHODS AND

findingsIn this retrospective cohort study, we developed and validated SurvFinder, an interpretable deep learning framework designed to autonomously identify tissue-based risk biomarkers from hematoxylin and eosin (H&E)-stained slides. The framework aims to support individualized risk stratification and explore associations with treatment outcomes. The present study included 6,950 H&E slides from 1,604 patients with stage II CRC across four independent cohorts in China. Patients were enrolled from 2012 to 2018 and followed for a minimum of 24 months. The primary outcome of the study was relapse-free survival (RFS). Our analyses identified tertiary lymphoid structures (TLSs) as critical prognostic features in stage II CRC. The multi-view integration of TLS characteristics by SurvFinder consistently demonstrated superior predictive and prognostic accuracy across four multicenter datasets (AUROC with 95% confidence interval [CI]: 0.827 [0.789,0.864], 0.805 [0.749,0.860], 0.805 [0.748,0.861], and 0.712 [0.621,0.804]), surpassing traditional clinical prognostic parameters (hazard ratio [HR]: 8.23, 95% CI: 5.43-12.47; p < 0.001). Using explainable AI (XAI) methods, we ensured model transparency and identified key TLS features-such as their location at the tumor periphery and their maturity state-as significant factors influencing prognosis and the efficacy of adjuvant therapy. The retrospective design without prospective validation and real-world clinical deployment is the main limitation of this study.

conclusionsTogether, these results highlight the potential utility of deep learning-based histopathological analysis for automated risk stratification in stage II CRC. In particular, our findings support the relevance of TLSs as a histological biomarker with potential implications for personalizing ACT decisions.