Multiparametric MRI radiomics combined with clinical-radiologic features improves preoperative prediction of muscle invasion in bladder cancer

Background Accurate preoperative differentiation of muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC) is central to treatment selection. We evaluated whether a nomogram integrating multiparametric MRI (mpMRI) radiomics, VI-RADS, and routinely recorded clinical-radiologic features improves prediction of muscle invasion. Methods This single-center retrospective study included 350 consecutive patients with histologically confirmed bladder urothelial carcinoma who underwent preoperative 3.0-T mpMRI between January 2019 and December 2024. Patients were split chronologically into a training cohort (n = 245) and a temporal validation cohort (n = 105). Radiomics features were extracted from T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps. Reproducibility filtering, univariable screening, collinearity pruning, and least-absolute-shrinkage-and-selection-operator regression were performed using the training cohort only. Clinical-radiologic, radiomics, and combined logistic models were evaluated by discrimination, calibration, Brier score, and decision curve analysis. Results MIBC was present in 78 of 245 training patients (31.8%) and 33 of 105 validation patients (31.4%). Rad-score, VI-RADS score, tumor size, hydronephrosis, and absence of a tumor stalk remained independently associated with muscle invasion. The combined nomogram achieved AUCs of 0.929 (95% CI 0.895-0.963) in training and 0.897 (95% CI 0.839-0.955) in validation, compared with 0.708 for the clinical-radiologic model, 0.798 for VI-RADS, and 0.838 for the radiomics model in validation. Among the 59 patients with a research VI-RADS score of exactly 3, the combined nomogram achieved AUCs of 0.871 (95% CI 0.751-0.991) in training and 0.835 (95% CI 0.625-0.999) in validation; the pooled descriptive AUC was 0.864 (95% CI 0.764-0.964). Because VI-RADS was constant within this subgroup, an informative VI-RADS-alone ROC curve could not be estimated. Validation calibration intercept and slope for the combined model were 0.06 (95% CI -0.20 to 0.32) and 0.91 (95% CI 0.70-1.13), respectively. Conclusion A combined mpMRI radiomics-clinical-radiologic nomogram showed strong temporal-validation performance for preoperative MIBC prediction, with an exploratory signal of utility in VI-RADS 3 lesions. External multicenter validation is required before clinical implementation.

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Journal
Frontiers in Oncology
Published
2026-10-26
DOI
https://doi.org/10.3389/fonc.2026.1949830
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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article

Multiparametric MRI radiomics combined with clinical-radiologic features improves preoperative prediction of muscle invasion in bladder cancer

Zhihui Qu, Lei Qu, Gang Yuan, Xuerong Ye et al.
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
article

Multiparametric MRI radiomics combined with clinical-radiologic features improves preoperative prediction of muscle invasion in bladder cancer

Zhihui Qu, Lei Qu, Gang Yuan, Xuerong Ye, Liran Wang
article en

Abstract

Background Accurate preoperative differentiation of muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC) is central to treatment selection. We evaluated whether a nomogram integrating multiparametric MRI (mpMRI) radiomics, VI-RADS, and routinely recorded clinical-radiologic features improves prediction of muscle invasion. Methods This single-center retrospective study included 350 consecutive patients with histologically confirmed bladder urothelial carcinoma who underwent preoperative 3.0-T mpMRI between January 2019 and December 2024. Patients were split chronologically into a training cohort (n = 245) and a temporal validation cohort (n = 105). Radiomics features were extracted from T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps. Reproducibility filtering, univariable screening, collinearity pruning, and least-absolute-shrinkage-and-selection-operator regression were performed using the training cohort only. Clinical-radiologic, radiomics, and combined logistic models were evaluated by discrimination, calibration, Brier score, and decision curve analysis. Results MIBC was present in 78 of 245 training patients (31.8%) and 33 of 105 validation patients (31.4%). Rad-score, VI-RADS score, tumor size, hydronephrosis, and absence of a tumor stalk remained independently associated with muscle invasion. The combined nomogram achieved AUCs of 0.929 (95% CI 0.895-0.963) in training and 0.897 (95% CI 0.839-0.955) in validation, compared with 0.708 for the clinical-radiologic model, 0.798 for VI-RADS, and 0.838 for the radiomics model in validation. Among the 59 patients with a research VI-RADS score of exactly 3, the combined nomogram achieved AUCs of 0.871 (95% CI 0.751-0.991) in training and 0.835 (95% CI 0.625-0.999) in validation; the pooled descriptive AUC was 0.864 (95% CI 0.764-0.964). Because VI-RADS was constant within this subgroup, an informative VI-RADS-alone ROC curve could not be estimated. Validation calibration intercept and slope for the combined model were 0.06 (95% CI -0.20 to 0.32) and 0.91 (95% CI 0.70-1.13), respectively. Conclusion A combined mpMRI radiomics-clinical-radiologic nomogram showed strong temporal-validation performance for preoperative MIBC prediction, with an exploratory signal of utility in VI-RADS 3 lesions. External multicenter validation is required before clinical implementation.

Frontiers in OncologyVol. 16
Qianjiang Central Hospital (CN), Chinese People's Liberation Army (CN)
Openalex Percentile: Top 13%
Radiomics and Machine Learning in Medical Imaging
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