Multimodal deep learning framework integrating multiphase CT and histopathological whole slide imaging for predicting recurrence in ccRCC.

ABSTRACT: ccRCC is an aggressive, heterogeneous tumor with a poor prognosis. Prognostic assessments need multi-modal data. Radiological images have limits, while pathological images offer micro-level details. Integrating these for ccRCC outcome prediction is important. Our study aimed to develop and validate a DL fusion model using multiphase CT images and WSI for postoperative risk stratification in ccRCC patients. This retrospective study included 274 ccRCC patients who underwent multiphase CT scans (Jan 2008-Mar 2021), with diagnoses confirmed by histopathology post-surgery. The patient cohort was divided into a training cohort of 164 patients for model development and a test cohort of 110 patients for model validation. The primary outcome was local recurrence or metastasis versus non-recurrence

SUBMITTER: Ma C 

PROVIDER: S-EPMC12638770 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature