Integrating radiomics with the vesical imaging-reporting and data system to predict muscle invasion of bladder cancer.

Abstract

To develop predictive models based on the integration of radiomics with the Vesical Imaging-Reporting and Data System (VI-RADS) for determining muscle invasion of bladder cancer. One hundred ninety-one patients were retrospectively included in this study from January 2015 to March 2022. Of these, 121 data were randomly divided into training and validation sets at a ratio of 7:3. The remaining data (n = 70) served as the independent testing set. The radiomics features were extracted from bladder cancer on high-b-value DWI images. The pipelines of radiomics models were trained in the training set. One optimal model was selected based on the performance in the validation set. Then, the selected model was tested in the independent testing set. Two radiologists evaluated the VI-RADS based on T2WI and DWI. Reader 1 was an experienced reader, and Reader 2 was an inexperienced reader. A clinical-radiomics model was built by integrating the radiomics signature and VI-RADS. The performance was assessed using receiver operating characteristic curve analysis. The histopathological results were used as the standard reference to assess the diagnostic accuracy of muscle invasion. The radiomics model had area under the curve (AUC) values of 0.801, 0.867, and 0.806 in the training, validation, and testing sets, respectively. The VI-RADS scores of Readers 1/2 yielded AUC values of 0.831/0.781, 0.909/0.815, and 0.871/0.776 in the training, validation, and testing sets, respectively. The clinical-radiomics model for Readers 1/2 revealed AUC values of 0.889/0.854, 0.961/0.919, and 0.881/0.844 in the training, validation, and testing sets, respectively. The performance of the clinical-radiomics model was improved compared to the VI-RADS score for inexperienced Reader 2 (P < 0.05). The radiomics model was useful in the diagnosis of muscle invasion of bladder cancer. The clinical-radiomics model integrating radiomics and VI-RADS further improved the performance compared to VI-RADS alone, which was helpful for readers with less diagnostic experience.