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Non-invasive evaluation of muscle invasion and survival prognosis in bladder cancer using enhanced CT-based deep learning radiomics: a multi-center real-world cohort study
RESEARCH | 更新时间:2026-09-15
Non-invasive evaluation of muscle invasion and survival prognosis in bladder cancer using enhanced CT-based deep learning radiomics: a multi-center real-world cohort study
Non-invasive evaluation of muscle invasion and survival prognosis in bladder cancer using enhanced CT-based deep learning radiomics: a multi-center real-world cohort study
- “一个研究小组介绍了其在人工智能辅助膀胱癌症诊断方面的进展。研究人员建立了一个结合CT放射学和深度学习的混合AI模型,为解决术前肌肉侵袭评估的局限性提供了解决方案,并为精确肿瘤学应用奠定了基础。”
- 军事医学研究(英文) 2026年13卷第8期 页码:1205-1221
DOI: 10.1016/j.mmr.2026.100001
录用:2026-03-05
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相关作者
William C. Cho
Jian Song
Guang-Chao Wang
Si-Cheng Wang
Chong-Ru He
Ying-Ze Zhang
Xiao Chen
Jia-Can Su
相关机构
Department of Clinical Oncology, Queen Elizabeth Hospital
Department of Orthopedics, Trauma Orthopedics Center, Institute of Musculoskeletal Injury and Translational Medicine of Organoids, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Department of Orthopedics, Shanghai Zhongye Hospital
Department of Orthopedics, Orthopedic Research Institution of Hebei Province, NHC Key Laboratory of Intelligent Orthopedic Equipment, The Third Hospital of Hebei Medical University
Institute of Translational Medicine, Organoid Research Center, National Center for Translational Medicine (Shanghai), Shanghai University Branch

