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Volume 11
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10.3390/jimaging11100342
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Article

Radiomics-Based Preoperative Assessment of Muscle-Invasive Bladder Cancer Using Combined T2 and ADC MRI: A Multicohort Validation Study

by
Dmitry Kabanov
1,*,
Natalia Rubtsova
1,
Aleksandra Golbits
2,
Andrey Kaprin
1,3,
Valentin Sinitsyn
4 and
Mikhail Potievskiy
5
1
Department of Computed Tomography and Magnetic Resonance Imaging, P. Hertsen Moscow Oncology Research Institute (MORI), 125284 Moscow, Russia
2
Department of Computed Tomography and Magnetic Resonance Imaging, N. Lopatkin Scientific Research Institute of Urology and Interventional Radiology (SRIUIR), 105425 Moscow, Russia
3
Department of Oncology and Radiology, Institute of Medicine, Peoples’ Friendship University of Russia—RUDN University, 117198 Moscow, Russia
4
Radiology Department of University Medical Center, Lomonosov Moscow State University, 119991 Moscow, Russia
5
Center for Clinical Trials of Center for Innovative Radiological and Regenerative Technologies, Federal State Budgetary Institution National Medical Research Radiological Centre of the Ministry of Health of the Russian Federation, 249031 Obninsk, Russia
*
Author to whom correspondence should be addressed.
J. Imaging 2025, 11(10), 342; https://doi.org/10.3390/jimaging11100342
Submission received: 9 August 2025 / Revised: 22 September 2025 / Accepted: 25 September 2025 / Published: 1 October 2025
(This article belongs to the Topic Machine Learning and Deep Learning in Medical Imaging)
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Abstract

Accurate preoperative staging of bladder cancer on MRI remains challenging because visual reads vary across observers. We investigated a multiparametric MRI (mpMRI) radiomics approach to predict muscle invasion (≥T2) and prospectively tested it on a validation cohort. Eighty-four patients with urothelial carcinoma underwent 1.5-T mpMRI per VI-RADS (T2-weighted imaging and DWI-derived ADC maps). Two blinded radiologists performed 3D tumor segmentation; 37 features per sequence were extracted (LifeX) using absolute resampling. In the training cohort (n = 40), features that differed between non-muscle-invasive and muscle-invasive tumors (Mann–Whitney p < 0.05) underwent ROC analysis with cut-offs defined by the Youden index. A compact descriptor combining GLRLM-LRLGE from T2 and GLRLM-SRLGE from ADC was then fixed and applied without re-selection to a prospective validation cohort (n = 44). Histopathology within 6 weeks—TURBT or cystectomy—served as the reference. Eleven T2-based and fifteen ADC-based features pointed to invasion; DWI texture features were not informative. The descriptor yielded AUCs of 0.934 (training) and 0.871 (validation) with 85.7% sensitivity and 96.2% specificity in validation. Collectively, these findings indicate that combined T2/ADC radiomics can provide high diagnostic accuracy and may serve as a useful decision support tool, after multicenter, multi-vendor validation.
Keywords: radiomics; bladder cancer; MRI; texture analysis; muscle invasion; staging; MIBC; LifeX radiomics; bladder cancer; MRI; texture analysis; muscle invasion; staging; MIBC; LifeX

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MDPI and ACS Style

Kabanov, D.; Rubtsova, N.; Golbits, A.; Kaprin, A.; Sinitsyn, V.; Potievskiy, M. Radiomics-Based Preoperative Assessment of Muscle-Invasive Bladder Cancer Using Combined T2 and ADC MRI: A Multicohort Validation Study. J. Imaging 2025, 11, 342. https://doi.org/10.3390/jimaging11100342

AMA Style

Kabanov D, Rubtsova N, Golbits A, Kaprin A, Sinitsyn V, Potievskiy M. Radiomics-Based Preoperative Assessment of Muscle-Invasive Bladder Cancer Using Combined T2 and ADC MRI: A Multicohort Validation Study. Journal of Imaging. 2025; 11(10):342. https://doi.org/10.3390/jimaging11100342

Chicago/Turabian Style

Kabanov, Dmitry, Natalia Rubtsova, Aleksandra Golbits, Andrey Kaprin, Valentin Sinitsyn, and Mikhail Potievskiy. 2025. "Radiomics-Based Preoperative Assessment of Muscle-Invasive Bladder Cancer Using Combined T2 and ADC MRI: A Multicohort Validation Study" Journal of Imaging 11, no. 10: 342. https://doi.org/10.3390/jimaging11100342

APA Style

Kabanov, D., Rubtsova, N., Golbits, A., Kaprin, A., Sinitsyn, V., & Potievskiy, M. (2025). Radiomics-Based Preoperative Assessment of Muscle-Invasive Bladder Cancer Using Combined T2 and ADC MRI: A Multicohort Validation Study. Journal of Imaging, 11(10), 342. https://doi.org/10.3390/jimaging11100342

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