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

作者：Z. Zhao, Dexia Chen, Ruixuan Wang, Xinke Zhang, XIAOBO WEN, Xueyi Zheng, Shasha Liu, Hao Chen, Yuqian Zhang, Donghui Huang, Chengyou Zheng, Mengke Ma, Dan Xie, Yan Sun, Xiaosheng He, Muyan Cai · 发表于：PLoS Medicine · 年份：2026 · DOI：10.1371/journal.pmed.1004614 · 被引用次数：6 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Colorectal Cancer Surgical Treatments

BACKGROUND: Approximately 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 FINDINGS: In 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]...