Journal of Clinical Practice · Published 2026-06-28 · DOI 10.17816/clinpract696013
Anastasia A. Kovalenko, Valentin E. Sinitsyn, Victor S. Petrovichev
BACKGROUND: Bladder cancer is one of the most common urinary malignancies. Tumor grade is a key predictor for treatment strategy. In addition to a histopathological examination after transurethral resection, texture analysis of magnetic resonance images (MRI) is used to grade tumors. AIM: The study aimed to evaluate the diagnostic value of texture analysis of MR images in differentiating well-differentiated/moderately differentiated (G1/2) and poorly differentiated (G3) bladder cancers, compare the performance of 2D and 3D segmentation techniques, compare different algorithms (LASSO, LASSO AutoML, Extra Trees, KNN, Random Forest, XGBoost, and LightXGB) in constructing predictive models based on texture features, and select the most optimal one based on the main performance metrics. METHODS: This retrospective study included 95 patients with bladder cancer. The patient data were randomly divided into a training set and a test set in a ratio of 80:20. A standard pelvic MRI scan with intravenous contrast enhancement was performed using 1.5/3 T scanners. All images were preprocessed by resampling to a fixed voxel size of 1 mm × 1 mm × 1 mm. Texture analysis included three pulse sequences: T2-weighted images, diffusion-weighted images with a b-value of 800/1000 s/mm2, and apparent diffusion coefficient maps. Both 2D and 3D segmentation was performed. RESULTS: The XGBoost (eXtreme Gradient Boosting) algorithm outperformed other 2D and 3D radiomics models for grading bladder cancer. The 2D XGBoost radiomics model included 39 texture metrics. On the test set, the model had an area under the curve (AUC) of 72.9%, accuracy of 78.9%, sensitivity of 80%, and specificity of 78.6%. The 3D XGBoost radiomics model included 23 texture metrics. On the test set, this model had an AUC of 82.9%, accuracy of 78.9%, sensitivity of 80%, and specificity of 78.6%. CONCLUSION: Texture analysis of MR images can be used to grade bladder cancer. Among predictive radiomics models, the XGBoost algorithm demonstrated the highest predictive value. Both 2D and 3D segmentation allow for the same level of accuracy in predicting tumor grade.
Abstract from DOAJ. Public domain (CC0 1.0).
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Kovalenko, A., Sinitsyn, V., Petrovichev, V. (2026). MRI radiomics for the prediction of histologic grade in bladder cancer. Journal of Clinical Practice. https://doi.org/10.17816/clinpract696013