Multimodal radiopathomics model predicts postoperative metachronous liver metastasis in gastric cancer

Abstract

In locally advanced gastric cancer, a substantial number of patients relapse with liver metastasis months after an apparently curative operation, and standard tumor staging offers little warning of who is at risk. Here, we develop the Radiopathomics-Clinical Stratification Assessment (RCSA), an interpretable model that integrates three complementary sources of information: radiomic features from preoperative computed tomography, pathomic features from routine hematoxylin and eosin tumor slides, and conventional clinical features. Trained on patients from one hospital and then tested on separate internal, external, public, and prospective trial groups (NCT02555358), RCSA consistently separates high- and low-risk patients, with area under the curves between 0.862 and 0.909. Tumors it labels low-risk carry a notably more active immune environment, indicating that these patients are the ones most likely to gain from added immunotherapy. RCSA therefore turns existing hospital data into individualized guidance for postoperative follow-up and treatment. Accurate prediction of metachronous liver metastasis (MLM) after curative surgery for locally advanced gastric cancer (LAGC) remains challenging. Here, the authors develop a multimodal model to predict MLM in LAGC patients by integrating clinical, imaging, and pathology data across multi-centre cohorts, also revealing patients who could be more responsive to adjuvant immunotherapy.