VI-RADS combined with decision tree model preoperatively predict the pathological grade of bladder cancer with high (≥3) and low (≤2) scores

Bladder cancer pathology grading is currently obtained mainly by invasive cystoscopic biopsy or surgical pathology. This study aimed to investigate the effectiveness of Vesical Imaging Reporting and Data System (VI-RADS) in the diagnosis of high-grade bladder cancer (HG-BC) and explore a new preoperative non-invasive grading prediction system. We demonstrate that most bladder cancers with a VI-RADS score ≥3 are high-grade and that the decision tree model is a good predictor of pathological grading in patients with VI-RADS ≤2. Thus, VI-RADS can be a grouping imaging biomarker for noninvasive prediction of bladder cancer grade.