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

作者：Lingkai Cai, Rongjie Bai, Qiang Cao, Weijie Sun, Fei Wang, Xiaotong Liu, Bo Liang, Meihua Jiang, Gongcheng Wang, Qiang Shao, Xuping Jiang, Chenghao Wang, Chang Chen, Zhengye Tan, Qikai Wu, Meiling Bao, Hao Yu, Pengchao Li, Xiao Yang, Qiang Lü · 发表于：EClinicalMedicine · 年份：2025 · DOI：10.1016/j.eclinm.2025.103640 · 被引用次数：3 · 研究领域：Bladder and Urothelial Cancer Treatments、Prostate Cancer Diagnosis and Treatment、MRI in cancer diagnosis

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 data modalities: three-dimensional (3D) deep learning features using a modified 3D ResNet50 network, 3D radiomics features, morphological MRI features, and clinical features. Patients were stratified into low- and high-risk prognostic groups based on MF-DLM scores, and model interpretability was evaluated using Shapley additive explanations (SHAP) and Gradient-weighted class activation mapping (Grad-CAM). Findings: The median follow-up time for the training, validation, and external test sets are 38.0 months (interquartile ranges [IQR]: 22.0, 62.0), 40.5 months (IQR: 23.0, 71.0), and 38.5 months (IQR: 26.0, 50.0), respectively. The MF-DLM demonstrated excellent performance in predicting OS, achieving high...