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MRI radiomics for predicting poor disease-free survival in muscle invasive bladder cancer: the results of the retrospective cohort study

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop an MRI radiomic nomogram capable of identifying muscle invasive bladder cancer (MIBC) patients with high-risk molecular characteristics related to poor 2-year disease-free survival (DFS).

Methods

We performed a retrospective analysis of DNA sequencing data, prognostic information, and radiomics features from 91 MIBC patients at stages T2-T4aN0M0 without history of immunotherapy. To identify risk stratification, we employed Cox regression based on TP53 mutation status and tumor mutational burden (TMB) level. Radiomics signatures were selected using the least absolute shrinkage and selection operator (LASSO) to construct a nomogram based on logistic regression for predicting the stratification in the training cohort. The predictive performance of the nomogram was assessed in the testing cohort using receiver operator curve (ROC), Hosmer–Lemeshow (HL) test, clinical impact curve (CIC), and decision curve analysis (DCA).

Results

Among 91 participants, the mean TMB value was 3.3 mut/Mb, with 60 participants having TP53 mutations. Patients with TP53 mutations and a below-average TMB value were identified as high risk and had a significantly poor 2-year DFS (hazard ratio = 4.36, 95% CI 1.82–10.44, P < 0.001). LASSO identified five radiomics signatures that correlated with the risk stratification. In the testing cohort, the nomogram achieved an area under the ROC curve of 0.909 (95% CI 0.789–0.991) and an accuracy of 0.889 (95% CI 0.708–0.977).

Conclusion

The molecular risk stratification based on TP53 mutation status combined with TMB level is strongly associated with DFS in MIBC. Radiomics signatures can effectively predict this stratification and provide valuable information to clinical decision-making.

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Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 81971592); the Four “Batches” Innovation Project of Invigorating Medical through Science and Technology of Shanxi Province (2023XM011); the China International Medical Foundation of China (z-2014-07-2301).

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Author notes

  1. Zhi-chang Fan and Lu Zhang have contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People’s Republic of China

    Zhi-chang Fan, Lu Zhang, Shuo Li, Jun-ting Guo & Jing-jing Bai

  2. Department of Radiology, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People’s Republic of China

    Guo-qiang Yang, Bin Wang, Yan Li, Le Wang & Xiao-chun Wang

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  1. Zhi-chang Fan
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Fan, Zc., Zhang, L., Yang, Gq. et al. MRI radiomics for predicting poor disease-free survival in muscle invasive bladder cancer: the results of the retrospective cohort study. Abdom Radiol 49, 151–162 (2024). https://doi.org/10.1007/s00261-023-04028-3

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