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

Summary: 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 retrosp...

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Principais autores: 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 Lu
Formato: Artigo
Idioma:Inglês
Publicado em: Elsevier 2025-12-01
coleção:EClinicalMedicine
Assuntos:
Bladder cancer
MRI
Prognosis
Deep learning
Acesso em linha:http://www.sciencedirect.com/science/article/pii/S2589537025005747
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