ArticleEClinicalMedicine2025
A non-invasive MRI-based multimodal fusion deep learning model (MF-DLM) for predicting overall survival in bladder cancer: a multicentre retrospective study.
Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application Value of Radiomics-Based Machine Learning for Preoperative Risk Stratification of Bladder Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- An Immunohistochemistry-Based Molecular Subtyping Approach for Capturing Clinical Outcome Heterogeneity in Bladder Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- MRI-based deep learning combined with radiomics for the preoperative prediction of lymphovascular invasion in patients with bladder cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Review
- Development and validation of MRI-based models to predict lymph node metastasis in bladder cancer: a multi-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
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20 authors.
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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 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 higher C-index values than pathological T stage (training: 0.902 vs. 0.793, p < 0.001; validation: 0.864 vs. 0.757, p = 0.014; external test: 0.841 vs. 0.760, p = 0.047). In addition, MF-DLM-based low-risk group demonstrated significantly longer OS in the training, validation, and external test sets (p < 0.001). In the adjuvant therapy (AT) cohort, high-risk patients had significantly worse prognosis compared with low-risk patients (p < 0.0001). Additionally, high-risk pathological T3/4 patients exhibited no statistically significant OS difference between those who received AT and those who did not (p = 0.18), whereas low-risk pathological T3/4 patients experienced significantly improved OS with AT (p = 0.0059). Besides, the low-risk group had better OS than the high-risk group in neoadjuvant therapy cohort (p = 0.0032). Interpretation: The MF-DLM can reliably predict OS in patients with BCa and provide additional prognostic stratification beyond pathological T and N stages. Furthermore, MF-DLM-based risk groups can identify patients most likely to benefit from perioperative therapy. Funding: The Noncommunicated Chronic Diseases-National Science and Technology Major Project (2024ZD0525700); National Natural Science Foundation of China (82273152, 82503879), Jiangsu Province Hospital (the First Affiliated Hospital of Nanjing Medical University) Clinical Capacity Enhancement Project (JSPH-MA-2022-5), China Postdoctoral Science Foundation funded project (2024M761211), and the Nanjing Postdoctoral Science Foundation funded project (2024BHS210).
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