Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader - 28/09/23
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Highlights |
• | In the peripheral zone, the best model for characterizing ISUP≥2 prostate cancer is a combination of 2nd ADC percentile and normalized wash-in rate. |
• | In the transition zone, the best model for characterizing ISUP≥2 prostate cancer uses the 25th ADC percentile. |
• | The zone-specific computer-aided diagnosis system combining the two best models provides diagnostic performance similar to that of PI-RADSv2 score in an internal and an external test dataset. |
• | The CAD score threshold yielding 90% sensitivity at training shows a sensitivity close to 90% in both internal and external test datasets. |
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.
El texto completo de este artículo está disponible en PDF.Key words : Artificial intelligence, Computer-assisted diagnosis, Magnetic resonance imaging, Prostatic neoplasms, Validation study
Abbreviations : ADC, ADC2, ADC25, AUC, CAD, CI, DCE, ISUP, mpMRI, PI-RADSv2, PZ, ROI, TZ, WI
Esquema
Vol 104 - N° 10
P. 465-476 - octobre 2023 Regresar al número
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