Development and validation of a deep learning–based pathomics signature for prognosis and chemotherapy benefits in colorectal cancer: A retrospective multicenter cohort study.

Abstract

298 Background: The current TNM staging system fails to provide adequate information for prognosis and adjuvant chemotherapy benefits in colorectal cancer (CRC). Pathomics, an emerging field, shows promise in improving prognosis estimation and decision-making. In this study, we developed and validated a pathomics signature (PS CRC ) that directly analyzes hematoxylin and eosin–stained slides using deep learning to predict outcomes. Methods: A total of 883 whole slide images from two cohorts, Harbin Medical University Cancer Hospital and The Cancer Genome Atlas (TCGA), were retrospectively analyzed. An interpretable, multi-instance deep learning model was proposed to establish the PS CRC . Shapley additive explanations were employed to interpret the model's decisions, and gradient-weighted class activation mapping was applied to visualise the pathological phenotypes of the PS CRC . The transcriptomics data from TCGA cohort was used to explore the potential pathogenesis underlying the PS CRC . Results: The PS CRC was identified as an independent prognostic factor associated with both overall survival and disease-free survival. Incorporating the PS CRC into the TNM stage model resulted in a significant improvement in prognosis estimation, as evidenced by a notable increase in net reclassification improvement and integrated discrimination improvement. Moreover, among stage II and III CRC patients with low levels of PS CRC , satisfactory benefits from chemotherapy were observed. Notably, the main underlying features of PS CRC include tumor cell infiltration, adipocyte accumulation, fibrous tissue deposition, and stromal infiltration. Transcriptome analysis further support the relevance of PS CRC to tumor progression and immune suppression. Conclusions: Our finds highlight the significant potential of histopathology images-based deep learning in predicting prognosis and assess therapeutic response of CRC. The PS CRC could serve as an effective tool in clinical decision for CRC management, providing insights into the underlying pathogenic mechanisms. However, prospective studies are still necessary for further validation.