The Vesical Imaging Reporting and Data System (VI-RADS) was launched in 2018 to standardize reporting of magnetic resonance imaging (MRI) for bladder cancer (BC). This study aimed to prospectively validate VI-RADS using a next-generation MRI scanner and to investigate the usefulness of denoising deep learning reconstruction (dDLR).
We prospectively enrolled 98 patients who underwent bladder multiparametric MRI using a next-generation MRI scanner before transurethral resection of bladder tumor (TURBT). Tumors were categorized according to VI-RADS, and we ultimately analyzed 68 patients with pathologically confirmed urothelial BC. We used receiving operating characteristic curve analyses to assess the predictive accuracy of VI-RADS for muscle invasion. Sensitivity, specificity, positive/negative predictive value, accuracy, and area under the curve (AUC) were calculated for different VI-RADS score cutoffs.
Muscle invasion was detected in the TURBT specimens of 18 patients (26%). The optimal cutoff value of the VI-RADS score was determined as≥4 based on the receiver operating curve analyses. The accuracy of diagnosing muscle invasion using a cutoff of VI-RADS ≥4 was 94% (AUC: 0.92). Additionally, we assessed the utility of dDLR: combination with dDLR significantly improved the AUC of category by T2-weighted imaging (T2WI), and of the four patients who were misdiagnosed by the final VI-RADS score, three were correctly diagnosed by T2WI+dDLR.
In this prospective validation study with a next-generation MRI scanner, VI-RADS showed high predictive accuracy for muscle invasion in patients with BC before TURBT. Combining T2WI with dDLR might further improve the diagnostic accuracy of VI-RADS.
The Journal of urology. 2020 Oct 06 [Epub ahead of print]
Satoru Taguchi, Mitsuhiro Tambo, Masanaka Watanabe, Haruhiko Machida, Toshiya Kariyasu, Keita Fukushima, Yuta Shimizu, Takatsugu Okegawa, Kenichi Yokoyama, Hiroshi Fukuhara
Department of Urology, Kyorin University School of Medicine, Tokyo, Japan., Department of Radiology, Kyorin University School of Medicine, Tokyo, Japan.