Multimodal deep learning model for prognostic prediction in cervical cancer receiving definitive radiotherapy: a multi-center study.

ABSTRACT: For patients with locally advanced cervical cancer (LACC), precise survival prediction models could guide personalized treatment. We developed and validated CerviPro, a deep learning-based multimodal prognostic model, to predict disease-free survival (DFS) in 1018 patients with LACC receiving definitive radiotherapy. The model integrates pre- and post-treatment CT imaging, handcrafted radiomic features, and clinical variables. CerviPro demonstrated robust predictive performance in the internal validation cohort (C-index 0.81), and external validation cohorts (C-index 0.70&0.66), significantly stratifying patients into distinct high- and low-risk DFS groups. Multimodal feature fusion consistently outperformed models based on single feature categories (clinical data, imaging, or radiomics al

SUBMITTER: Wang W 

PROVIDER: S-EPMC12322216 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature