Abstract
Accurate prognostic models are essential for optimizing treatment strategies in gallbladder adenocarcinoma (GBAC). We preliminarily constructed and validated a multimodal fusion model integrating clinical features, enhanced computed tomographic (CT) images, and digital histopathological images to predict postoperative survival. Data from 177 patients with GBAC who underwent surgery between January 2017 and May 2023 at two tertiary hospitals were retrospectively analyzed. A deep-learning radiomics model was developed using preoperative, portal-venous phase, contrast-enhanced CT images. A pathomics model was constructed from hematoxylin-and-eosin-stained whole-slide images using a multi-instance learning approach. The three single-modality models were integrated through late-fusion logistic regression to generate a multimodal nomogram. The deep-learning radiomics model achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.883. The pathomics and clinical models yielded AUC-ROCs of 0.780 and 0.693, respectively. The multimodal nomogram showed superior performance (AUC-ROC: 0.896; sensitivity: 0.903; specificity: 0.810; accuracy: 0.865; concordance index: 0.736). Calibration and decision curve analysis confirmed clinical utility. Kaplan-Meier analysis revealed notable survival differences between the high- and low-risk sub-cohorts. In this limited cohort, a multimodal model integrating CT-based radiomics, pathomics, and clinical features showed preliminary association with postoperative survival in GBAC, and has the potential to support individualized clinical risk stratification in the future.