A non-invasive MRI-based multimodal fusion deep learning model (MF-DLM) for predicting overall survival in bladder cancer: a multicentre retrospective study.

ABSTRACT:

Background

Accurate prognosis prediction in bladder cancer (BCa) is crucial for personalized treatment. This study aimed to develop and validate a non-invasive model using magnetic resonance imaging (MRI) for predicting the overall survival (OS) in patients with BCa.

Methods

This retrospective multicentre study included 1131 patients with BCa from eight institutions in China from June 2011 to March 2024. 871 patients were enrolled from one centre, who were randomly divided (8:2) into training (n = 697) and internal validation (n = 174) sets. For the external test set, 260 patients with BCa from seven centres were retrospectively included. We developed a multimodal fusion deep learning model (MF-DLM), leveraging a cross-attention mechanism to integrate four key preoperative d

SUBMITTER: Cai L 

PROVIDER: S-EPMC12661355 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature