A Multimodal, Multitask Prediction Framework for Diagnosis and Prognosis of Clear Cell Renal Cell Carcinoma

The purpose of this study is to develop and validate a multimodal, multitask prediction framework for clear cell renal cell carcinoma (ccRCC) by integrating preoperative CT radiomics, pathology-derived biomarker data from preoperative biopsy specimens, and clinical variables. The model was built for pathologically confirmed ccRCC and excluded other RCC histologic subtypes (e.g., papillary and chromophobe). In this multicenter retrospective study, ccRCC patients were enrolled and data were collected from preoperative CT scans, AMACR/P504S immunohistochemistry results, pathology reports, and follow-up records. Patients were assigned by hospital site into a training cohort and an independent external test cohort. Radiomic features were extracted from CT tumor regions of interest (ROIs), while deep learning features were derived from pathology images. Clinical variables were incorporated as additional inputs. A post-feature fusion strategy enabled simultaneous prediction of tumor classification and postoperative survival. Model performance was assessed using AUC for diagnosis and C-index for survival, together with calibration curves, decision curve analysis, and bootstrap confidence intervals. SHAP analysis was applied to quantify feature contributions. In external validation, the integrated multimodal model achieved strong diagnostic discrimination for P504S/AMACR (AUC = 0.983) and demonstrated improved prognostic performance for postoperative outcomes (C-index = 0.804). Subgroup analyses by grade and stage further supported model robustness, while SHAP-based interpretation indicated complementary contributions from imaging, pathology, and clinical variables. Overall, the proposed multimodal, multitask fusion framework enables reliable preoperative P504S/AMACR-based classification and postoperative prognostic prediction in ccRCC, supporting more refined risk stratification and individualized postoperative management using noninvasive imaging and clinical information. Calibration and decision curve analyses further support its potential clinical utility. Larger prospective and external multicenter validations are still needed to confirm generalizability.