Performance of a Region of Interest–based Algorithm in Diagnosing International Society of Urological Pathology Grade Group ≥2 Prostate Cancer on the MRI-FIRST Database—CAD-FIRST Study - Archive ouverte HAL Access content directly
Journal Articles European Urology Oncology Year : 2024

Performance of a Region of Interest–based Algorithm in Diagnosing International Society of Urological Pathology Grade Group ≥2 Prostate Cancer on the MRI-FIRST Database—CAD-FIRST Study

Nicolas Arfi
  • Function : Author
Flavie Bratan
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Hervé Lang
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Frédéric Lefèvre
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Clément Marcelin
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Paul Moldovan
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Nicolas Mottet
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Pierre Mozer
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Eric Potiron
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Daniel Portalez
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Philippe Puech
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Raphaele Renard-Penna
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Catherine Roy
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Marc-Olivier Timsit
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Thibault Tricard
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Arnauld Villers
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Jochen Walz
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Florence Mège-Lechevallier
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Myriam Decaussin-Petrucci
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Lionel Badet
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Marc Colombel
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Alain Ruffion
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Sébastien Crouzet
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Olivier Rouvière
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Abstract

Background and objective: Prostate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest-based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI. Methods: The lesions targeted at biopsy in the MRI-FIRST dataset were retrospectively delineated and assessed using a previously developed algorithm. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score assigned prospectively before biopsy and the algorithm score calculated retrospectively in the regions of interest were compared for diagnosing GG ≥2 cancer, using the areas under the curve (AUCs), and sensitivities and specificities calculated with predefined thresholds (PIRADSv2 scores ≥3 and ≥4; algorithm scores yielding 90% sensitivity in the training database). Ten predefined biopsy strategies were assessed retrospectively. Key findings and limitations: After excluding 19 patients, we analysed 232 patients imaged on 16 different scanners; 85 had GG ≥2 cancer at biopsy. At patient level, AUCs of the algorithm and PI-RADSv2 were 77% (95% confidence interval [CI]: 70-82) and 80% (CI: 74-85; p = 0.36), respectively. The algorithm's sensitivity and specificity were 86% (CI: 76-93) and 65% (CI: 54-73), respectively. PI-RADSv2 sensitivities and specificities were 95% (CI: 89-100) and 38% (CI: 26-47), and 89% (CI: 79-96) and 47% (CI: 35-57) for thresholds of ≥3 and ≥4, respectively. Using the PI-RADSv2 score to trigger a biopsy would have avoided 26-34% of biopsies while missing 5-11% of GG ≥2 cancers. Combining prostate-specific antigen density, the PI-RADSv2 and algorithm's scores would have avoided 44-47% of biopsies while missing 6-9% of GG ≥2 cancers. Limitations include the retrospective nature of the study and a lack of PI-RADS version 2.1 assessment. Conclusions and clinical implications: The algorithm provided robust results in the multicentre multiscanner MRI-FIRST database and could help select patients for biopsy. Patient summary: An artificial intelligence-based algorithm aimed at diagnosing aggressive cancers on prostate magnetic resonance imaging showed results similar to expert human assessment in a prospectively acquired multicentre test database.

Dates and versions

hal-04549487 , version 1 (17-04-2024)

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Thibaut Couchoux, Tristan Jaouen, Christelle Melodelima-Gonindard, Pierre Baseilhac, Arthur Branchu, et al.. Performance of a Region of Interest–based Algorithm in Diagnosing International Society of Urological Pathology Grade Group ≥2 Prostate Cancer on the MRI-FIRST Database—CAD-FIRST Study. European Urology Oncology, 2024, S2588-9311 (24), pp.00056-7. ⟨10.1016/j.euo.2024.03.003⟩. ⟨hal-04549487⟩
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