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<p>Histological grade holds great clinical significance in the management and prognosis of bladder cancer; therefore, timely and accurate prediction through non-invasive techniques such as MRI may improve health outcomes. Our objective was to create an MRI-based radiomics model that can predict the histological grade of cancer pre-operatively. In a prospective study, we gathered data from 45 bladder cancer patients who had an mpMRI scan from December 2018 to December 2022 prior to their operative procedure. Radiomics features were extracted from T2-weighted (T2W), diffusion-weighted imaging (DWI), and dynamic DCE-MRI-enhanced (DCE) MR images obtained from a 1.5&#xa0;T MRI scanner. A standard 5-point VI-RADS scoring system was also assessed for each scan. The variable clustering algorithm was applied to these features, and all cluster features were univariably assessed using receiver operating characteristic (ROC) curves. Multiple predictive models were created and cross-validated based on multivariable analysis to minimize overfitting and predict the grade of the tumor. Among 45 eligible patients, 28 (62.2%) patients had high-grade tumors and the rest 17 (37.8%) were low grade. In the adjusted analysis, only DCE-MRI based (Gray Level Co-occurrence Matrix (Gray Level Co-occurrence Matrix (GLCM))-inverse variance (OR = 1.42, <i>p</i> = 0.028), Major Axis Length (OR = 1.04, <i>p</i> = 0.03)), and age (OR = 1.08, <i>p</i> = 0.039) were associated with the high-grade bladder cancer. Our radiomics models comprising DCE-MRI-based parameters, a T2W parameter, and age yielded the highest performance for predicting grades of bladder cancer (AUC = 0.91; 95% CI 0.82–1.00). These models demonstrated reasonably high predictive performance in bootstrap validation analysis as well. An mpMRI radiomics approach based on MRI has the potential to serve as a non-invasive imaging tool for preoperative grading of bladder cancer.</p>

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Multiparametric MRI Radiomics for the Prediction of Histologic Grade in Bladder Cancer

  • 期刊论文

  • 发表日期：

    2025年8月6日

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  • Anup Selvaraju,
  • Durgesh Kumar Dwivedi,
  • Naveen Kumar Patbamniya,
  • Manoj Kumar,
  • Amlesh Seth,
  • Alok Kumar Dwivedi,
  • Rakesh Chandra Joshi,
  • Seema Kaushal,
  • Chandan J. Das

摘要

Histological grade holds great clinical significance in the management and prognosis of bladder cancer; therefore, timely and accurate prediction through non-invasive techniques such as MRI may improve health outcomes. Our objective was to create an MRI-based radiomics model that can predict the histological grade of cancer pre-operatively. In a prospective study, we gathered data from 45 bladder cancer patients who had an mpMRI scan from December 2018 to December 2022 prior to their operative procedure. Radiomics features were extracted from T2-weighted (T2W), diffusion-weighted imaging (DWI), and dynamic DCE-MRI-enhanced (DCE) MR images obtained from a 1.5 T MRI scanner. A standard 5-point VI-RADS scoring system was also assessed for each scan. The variable clustering algorithm was applied to these features, and all cluster features were univariably assessed using receiver operating characteristic (ROC) curves. Multiple predictive models were created and cross-validated based on multivariable analysis to minimize overfitting and predict the grade of the tumor. Among 45 eligible patients, 28 (62.2%) patients had high-grade tumors and the rest 17 (37.8%) were low grade. In the adjusted analysis, only DCE-MRI based (Gray Level Co-occurrence Matrix (Gray Level Co-occurrence Matrix (GLCM))-inverse variance (OR = 1.42, p = 0.028), Major Axis Length (OR = 1.04, p = 0.03)), and age (OR = 1.08, p = 0.039) were associated with the high-grade bladder cancer. Our radiomics models comprising DCE-MRI-based parameters, a T2W parameter, and age yielded the highest performance for predicting grades of bladder cancer (AUC = 0.91; 95% CI 0.82–1.00). These models demonstrated reasonably high predictive performance in bootstrap validation analysis as well. An mpMRI radiomics approach based on MRI has the potential to serve as a non-invasive imaging tool for preoperative grading of bladder cancer.