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

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 informati...

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Bibliographic Details
Published in:Abdominal imaging 2024-01, Vol.49 (1), p.151-162
Main Authors: Fan, Zhi-chang, Zhang, Lu, Yang, Guo-qiang, Li, Shuo, Guo, Jun-ting, Bai, Jing-jing, Wang, Bin, Li, Yan, Wang, Le, Wang, Xiao-chun
Format: Article
Language:English
Subjects:
Bladder
Bladder cancer
Cancer
Decision analysis
Decision making
Disease-Free Survival
DNA sequencing
Gastroenterology
Hepatology
Humans
Imaging
Immunotherapy
Invasiveness
Kidneys
Magnetic Resonance Imaging
Medicine
Medicine & Public Health
Methyl isobutyl carbinol
Muscles
Mutation
Neoplasms
Nomograms
Performance prediction
Radiology
Radiomics
Retroperitoneum
Retrospective Studies
Risk
Signatures
Survival
Ureters
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Summary: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. Graphical abstract
ISSN:2366-0058
2366-004X
2366-0058
DOI:10.1007/s00261-023-04028-3