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. 2023 Oct;104(10):465-476.
doi: 10.1016/j.diii.2023.04.006. Epub 2023 May 20.

Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader

Tristan Jaouen  1 , Rémi Souchon  1 , Paul C Moldovan  2 , Flavie Bratan  3 , Audrey Duran  4 , Au Hoang-Dinh  5 , Florian Di Franco  2 , Sabine Debeer  2 , Marine Dubreuil-Chambardel  2 , Nicolas Arfi  6 , Alain Ruffion  7 , Marc Colombel  8 , Sébastien Crouzet  9 , Christelle Gonindard-Melodelima  10 , Olivier Rouvière  11
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Free article

Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader

Tristan Jaouen et al. Diagn Interv Imaging. 2023 Oct.
Free article

Abstract

Purpose: The purpose of this study was to develop and test across various scanners a zone-specific region-of-interest (ROI)-based computer-aided diagnosis system (CAD) aimed at characterizing, on MRI, International Society of Urological Pathology (ISUP) grade≥2 prostate cancers.

Materials and methods: ROI-based quantitative models were selected in multi-vendor training (265 pre-prostatectomy MRIs) and pre-test (112 pre-biopsy MRIs) datasets. The best peripheral and transition zone models were combined and retrospectively assessed in internal (158 pre-biopsy MRIs) and external (104 pre-biopsy MRIs) test datasets. Two radiologists (R1/R2) retrospectively delineated the lesions targeted at biopsy in test datasets. The CAD area under the receiver operating characteristic curve (AUC) for characterizing ISUP≥2 cancers was compared to that of the Prostate Imaging-Reporting and Data System version2 (PI-RADSv2) score prospectively assigned to targeted lesions.

Results: The best models used the 25th apparent diffusion coefficient (ADC) percentile in transition zone and the 2nd ADC percentile and normalized wash-in rate in peripheral zone. The PI-RADSv2 AUCs were 82% (95% confidence interval [CI]: 74-87) and 86% (95% CI: 81-91) in the internal and external test datasets respectively. They were not different from the CAD AUCs obtained with R1 and R2 delineations, in the internal (82% [95% CI: 76-89], P = 0.95 and 85% [95% CI: 78-91], P = 0.55) and external (82% [95% CI: 74-91], P = 0.41 and 86% [95% CI:78-95], P = 0.98) test datasets. The CAD yielded sensitivities of 86-89% and 90-91%, and specificities of 64-65% and 69-75% in the internal and external test datasets respectively.

Conclusion: The CAD performance for characterizing ISUP grade≥2 prostate cancers on MRI is not different from that of PI-RADSv2 score across two test datasets.

Keywords: Artificial intelligence; Computer-assisted diagnosis; Magnetic resonance imaging; Prostatic neoplasms; Validation study.

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Conflict of interest statement

Conflict of interest Olivier Rouvière, Rémi Souchon, Tristan Jaouen, Au Hoang Dinh and Christelle Gonindard-Melodelima are inventors on international patent application PCT/EP2022/061221 "System for helping diagnosing aggressive prostate cancers and related method" that protects the algorithm described in this article.