Prospective Validation of Vesical Imaging-Reporting and Data System Using a Next-Generation Magnetic Resonance Imaging Scanner-Is Denoising Deep Learning Reconstruction Useful?
J Urol
; 205(3): 686-692, 2021 03.
Article
en En
| MEDLINE
| ID: mdl-33021428
ABSTRACT
PURPOSE:
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. MATERIALS ANDMETHODS:
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.RESULTS:
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.CONCLUSIONS:
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.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias de la Vejiga Urinaria
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Carcinoma de Células Transicionales
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Aprendizaje Profundo
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Imágenes de Resonancia Magnética Multiparamétrica
Tipo de estudio:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Aged
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Aged80
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Female
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Humans
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Male
Idioma:
En
Revista:
J Urol
Año:
2021
Tipo del documento:
Article
País de afiliación:
Japón