4.7 Article

Quantitation of bladder cancer for the prediction of muscle layer invasion as a complement to the vesical imaging-reporting and data system

Journal

EUROPEAN RADIOLOGY
Volume 31, Issue 3, Pages 1656-1666

Publisher

SPRINGER
DOI: 10.1007/s00330-020-07224-7

Keywords

Urinary bladder neoplasms; Carcinoma; transitional cell; Neoplasm staging; Magnetic resonance imaging

Funding

  1. National Research Foundation - Ministry of Science, Information and Communications Technology, Republic of Korea [NRF-2013R1A1A2011398]
  2. Seoul National University Bundang Hospital Research Fund [11-2010-008]
  3. National Research Foundation of Korea [2013R1A1A2011398] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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The study examined the diagnostic performance of VIRADS in predicting muscle layer invasion in bladder cancer patients and identified a quantitative indicator for predicting this invasion. VIRADS score and tumor-wall interface were found to be independently associated with muscle layer invasion. VIRADS showed good predictive ability for muscle layer invasion.
Objectives To examine the diagnostic performance of Vesical Imaging-Reporting and Data System (VIRADS) and to find a quantitative indicator for predicting muscle layer invasion of bladder cancer. Methods 3-T MRI of 82 patients performed before transurethral resection of bladder tumors or radical cystectomy between July 2018 and June 2019 were retrospectively analyzed. For one index lesion of each patient, two radiologists independently assigned VIRADS score and measured tumor-wall interface (contact length between tumor and bladder wall) on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI. Inter-reader agreement was assessed, and logistic regression analysis was performed to find indicators of muscle layer invasion. Comparison of indicators' diagnostic performance was done with receiver operating characteristic (ROC) curve and generalized linear model analyses. Optimal cutoff point was determined by the Youden indexJ. Results Inter-reader agreement was at least substantial for VIRADS categorization (kappa 0.77-0.81), and almost perfect for tumor-wall interface (intraclass correlation coefficient 0.88-0.90). Tumor-wall interface (odds ratio [OR] 1.90-2.00) and VIRADS score (OR 8.59-8.89) were independently associated with muscle layer invasion (p <= 0.02). For VIRADS, area under the ROC curve (AUROC) was 0.94, and the accuracy was 0.93 at score 3, the optimal threshold for predicting muscle layer invasion. Depending on the MRI sequence, tumor-wall interface showed AUROCs of 0.90-0.92 and accuracy of 0.84-0.90 at suggested thresholds (3 +/- 0.3 cm). Tumor-wall interface showed insignificant differences in accuracy compared with VIRADS (p> 0.10), except as measured on diffusion-weighted images (p= 0.01). Conclusions VIRADS is a good predictor of muscle layer invasion. As an independent quantitative indicator, tumor-wall interface may complement VIRADS to enhance prediction.

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