MRI evaluation of vesical imaging reporting and data system for bladder cancer after neoadjuvant chemotherapy.

ABSTRACT

BACKGROUND: The Vesical Imaging-Reporting and Data System (VI-RADS) has demonstrated effectiveness in predicting muscle invasion in bladder cancer before treatment. The urgent need currently is to evaluate the muscle invasion status after neoadjuvant chemotherapy (NAC) for bladder cancer. This study aims to ascertain the accuracy of VI-RADS in detecting muscle invasion post-NAC treatment and assess its diagnostic performance across readers with varying experience levels. METHODS: In this retrospective study, patients with muscle-invasive bladder cancer who underwent magnetic resonance imaging (MRI) after NAC from September 2015 to September 2018 were included. VI-RADS scores were independently assessed by five radiologists, consisting of three experienced in bladder MRI and two inexperienced radiologists. Comparison of VI-RADS scores was made with postoperative histopathological diagnosis. Receiver operating characteristic curve analysis (ROC) was used for evaluating diagnostic performance, calculating sensitivity, specificity, and area under ROC (AUC)). Interobserver agreement was assessed using the weighted kappa statistic. RESULTS: The final analysis included 46 patients (mean age 61 years ± 9 [standard deviation]; age range 39-70 years; 42 men). The pooled AUC for predicting muscle invasion was 0.945 (95% confidence interval (CI) 0.893-0.977) for experienced readers, and 0.910 (95% CI 0.831-0.959) for inexperienced readers, and 0.932 (95% CI 0.892-0.961) for all readers. At an optimal cut-off value ≥ 4, pooled sensitivity and specificity were 74.1% (range 66.0-80.9%) and 94.1% (range 88.6-97.7%) for experienced readers, and 63.9% (range 59.6-68.1%) and 86.4% (range 84.1-88.6%) for inexperienced readers. Interobserver agreement ranged from substantial to excellent between all readers (k = 0.79-0.92). CONCLUSIONS: VI-RADS accurately assesses muscle invasion in bladder cancer patients after NAC and exhibits good diagnostic performance across readers with different experience levels.