51. Prospective Validation of Vesical Imaging-Reporting and Data System Using a Next-Generation Magnetic Resonance Imaging Scanner—Is Denoising Deep Learning Reconstruction Useful?
- Author
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Toshiya Kariyasu, Takatsugu Okegawa, Haruhiko Machida, Satoru Taguchi, Kenichi Yokoyama, Masanaka Watanabe, Hiroshi Fukuhara, Mitsuhiro Tambo, Yuta Shimizu, and Keita Fukushima
- Subjects
Male ,medicine.medical_specialty ,Scanner ,Urology ,Noise reduction ,030232 urology & nephrology ,03 medical and health sciences ,Deep Learning ,0302 clinical medicine ,Predictive Value of Tests ,medicine ,Humans ,Medical physics ,Prospective Studies ,Multiparametric Magnetic Resonance Imaging ,Aged ,Aged, 80 and over ,Carcinoma, Transitional Cell ,Bladder cancer ,medicine.diagnostic_test ,business.industry ,Deep learning ,Magnetic resonance imaging ,medicine.disease ,Urinary Bladder Neoplasms ,Research Design ,Female ,Artificial intelligence ,Noise ,business - Abstract
The Vesical Imaging Reporting and Data System (VI-RADS) was launched in 2018 to standardize reporting of magnetic resonance imaging for bladder cancer. This study aimed to prospectively validate VI-RADS using a next-generation magnetic resonance imaging scanner and to investigate the usefulness of denoising deep learning reconstruction.We prospectively enrolled 98 patients who underwent bladder multiparametric magnetic resonance imaging using a next-generation magnetic resonance imaging scanner before transurethral resection of bladder tumor. Tumors were categorized according to VI-RADS, and we ultimately analyzed 68 patients with pathologically confirmed urothelial bladder cancer. 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 were calculated for different VI-RADS score cutoffs.Muscle invasion was detected in the transurethral resection of bladder tumor 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 denoising deep learning reconstruction. Combination with denoising deep learning reconstruction significantly improved the AUC of category by T2-weighted imaging, and of the 4 patients who were misdiagnosed by the final VI-RADS score 3 were correctly diagnosed by T2-weighted imaging+denoising deep learning reconstruction.In this prospective validation study with a next-generation magnetic resonance imaging scanner, VI-RADS showed high predictive accuracy for muscle invasion in patients with bladder cancer before transurethral resection of bladder tumor. Combining T2-weighted imaging with denoising deep learning reconstruction might further improve the diagnostic accuracy of VI-RADS.
- Published
- 2021