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

Yun-Bo He; Jiao Hu; Zhi Liu; Zi-Cheng Xiao; Jin-Hui Liu; Hai-Su Liang; Wen-Zhi Deng; Zhi-Wei Li; Jun Zhang; Jia-Quan Long; Ning Gao; Bin Huang; Xi Guo; Zhen-Yu Ou; Jin-Bo Chen; Pei-Hua Liu; Min-Feng Chen; Hui-Huang Li; Rui-Zhe Wang; Xiao Guan; Shi-Yu Tong; Yang-Le Li; Wei He; Yan-Hua Zhao; Zhi-Yong Cai; Yu Gan; Cheng Zhao; Yu Cui; Yuan-Qing Dai; Yi Cai; Zhen-Yu Nie; Wei-Min Zhou; Bo-Han Zhou; Ming-Hui Hu; Ben-Yi Fan; Ding-Shan Deng; Xiong-Bing Zu

doi:10.1016/j.mmr.2026.100001

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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 | Updated：2026-05-11

- 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
- Military Medical Research (2026)
- Affiliations：
  
  Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China
  National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
  Department of Urology, the Second Affiliated Hospital, Guizhou Medical University, Guiyang 550000, China
  Department of Pathology, the Third Xiangya Hospital of Central South University, Changsha 410013, China
  Department of Urology, the Second Hospital of University of South China, Hengyang 421001, Hunan, China
  Department of Imaging, the First People’s Hospital of Kaili, Kaili 556000, Guiyang, China
  Department of Imaging, the Second Affiliated Hospital, Guizhou Medical University, Kaili 556000, Guiyang, China
  Department of Urology, Xiangya Boai Rehabilitation Hospital, Changsha 410146, China
  Department of Urology, Hunan Provincial People’s Hospital/the First Affiliated Hospital of Hunan Normal University, Changsha 410005, China
- Author bio：
  
  Ben-Yi Fan, fanbenyi2009@yeah.net
  Ding-Shan Deng, dds15116217256@163.com;
  *Xiong-Bing Zu, zuxbxy@csu.edu.cn;
- Funds：
- DOI：10.1016/j.mmr.2026.100001
  CLC：
- Received：25 May 2025，
  
  Revised：2026-03-05，
  
  Published：2026-03
- Accepted：
Scan QR Code
He YB, Hu J, Liu Z, Xiao ZC, Liu JH, Liang HS, et al. 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. Mil Med Res. 2026;13(1):100001.
DOI：

He YB, Hu J, Liu Z, Xiao ZC, Liu JH, Liang HS, et al. 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. Mil Med Res. 2026;13(1):100001. DOI： 10.1016/j.mmr.2026.100001.

摘要

Abstract

Background:

Bladder cancer (BLCA) is a prevalent malignancy characterized by high recurrence and poor prognosis

particularly muscle-invasive bladder cancer (MIBC). Histopathology

the gold standard for assessing muscle invasion

often suffers from sampling errors and operator dependency

underscoring the need for non-invasive

accurate preoperative assessment methods. This study aimed to develop and validate a hybrid artificial intelligence (AI) model based on computed tomography (CT) radiomics and deep learning (DL) to predict MIBC and overall survival (OS) preoperatively in BLCA patients.

Methods:

A total of 1370 patients from 6 academic medical centers were retrospectively included. Preoperative contrast-enhanced CT scans were analyzed to extract handcrafted radiomic features using PyRadiomics and DL features using ResNet101

followed by machine learning (ML)-based modeling for prediction. A hybrid model combining radiomic and DL features was constructed and validated in internal and external cohorts. Model performance was evaluated using metrics such as the area under the curve (AUC) and Cox proportional hazards analysis for OS prediction.

Results:

The DL radiomics nomogram (DLRN) model demonstrated superior diagnostic performance

achieving an AUC of 0.807 in the internal validation cohort and 0.783 in the external multi-center validation cohort for predicting muscle invasion. The DLRN generated an imaging-derived risk score (DLRN score)

which was subsequently incorporated as one covariate into a multivariable Cox proportional hazards model together with clinicopathological variables to evaluate OS. Using this approach

patients were effectively stratified into high- and low-risk groups for OS

showing robust generalizability across diverse clinical settings. AI-assisted diagnostics significantly improved the sensitivity and accuracy of urologists

particularly among less experienced clinicians.

Conclusion:

The DLRN model provides a reliable

non-invasive tool for preoperative assessment of muscle invasion and prognosis in BLCA. Addressing histopathology limitations

it offers valuable insights for personalized treatment strategies

paving the way for precision oncology in real-world clinical applications.

关键词

Keywords

references

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