DeepLNM: A Multimodal Model for Predicting Lymph Node Metastasis in Colorectal Cancer

Accurate assessment of lymph node metastasis (LNM) is critical for prognostic stratification and treatment selection in colorectal cancer (CRC), yet imaging and routine pathology can yield false-negative/false-positive results. We propose DeepLNM, a multi-center foundation model for LNM prediction in CRC, evaluated with retrospective, external, prospective, and user study evidence. A multimodal dataset was assembled from three hospitals, with matched hematoxylin and eosin (H&E)-stained whole-slide images (WSI), CT scans, and text. DeepLNM achieved a mean AUC of 0.922 in the internal cohort and AUCs of 0.872 and 0.848 in two external cohorts. In a prospective cohort from a registered clinical trial (NCT07239063), DeepLNM remained robust (AUC = 0.845). In a user study, DeepLNM improved diagnostic accuracy, especially for junior pathologists (95.0%-97.3%), while reducing diagnostic time and increasing confidence. To explore the model's generalizability, it was further validated in a gastric cancer cohort, achieving an AUC of 0.793. Finally, DeepLNM identified histopathological features associated with LNM, addressing the "black-box" concern of deep learning. Together, these results position DeepLNM as a clinically preliminarily validated multimodal model for predicting LNM status in CRC, potentially facilitating the clinical translation of AI pathology.