The Journal of urology · 2025
An Artificial Intelligence Model Using Diffusion Basis Spectrum Imaging Metrics Accurately Predicts Clinically Significant Prostate Cancer.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Kim Eric H, Jing Huaping, Utt Kainen L, Vetter Joel M, Weimholt R Cody, Bullock Arnold D, Klim Alexandra P, Bergeron Karla A, Frankel Jason K, Smith Zachary L, Andriole Gerald L, Song Sheng-Kwei, Ippolito Joseph E
Аннотация
PURPOSE: Conventional prostate magnetic resonance imaging has limited accuracy for clinically significant prostate cancer (csPCa). We performed diffusion basis spectrum imaging (DBSI) before biopsy and applied artificial intelligence models to these DBSI metrics to predict csPCa.
MATERIALS AND METHODS: Between February 2020 and March 2024, 241 patients underwent prostate MRI that included conventional and DBSI-specific sequences before prostate biopsy. We used artificial intelligence models with DBSI metrics as input classifiers and the biopsy pathology as the ground truth. The DBSI-based model was compared with available biomarkers (PSA, PSA density [PSAD], and Prostate Imaging Reporting and Data System [PI-RADS]) for risk discrimination of csPCa defined as Gleason score > 7.
RESULTS: The DBSI-based model was an independent predictor of csPCa (odds ratio [OR] 2.04, 95% CI 1.52-2.73, P < .01), as were PSAD (OR 2.02, 95% CI 1.21-3.35, P = .01) and PI-RADS classification (OR 4.00, 95% CI 1.37-11.6 for PI-RADS 3, P = .01; OR 9.67, 95% CI 2.89-32.7 for PI-RADS 4-5, P < .01), adjusting for age, family history, and race. Within our dataset, the DBSI-based model alone performed similarly to PSAD + PI-RADS (AUC 0.863 vs 0.859, P = .89), while the combination of the DBSI-based model + PI-RADS had the highest risk discrimination for csPCa (AUC 0.894, P < .01). A clinical strategy using the DBSI-based model for patients with PI-RADS 1-3 could have reduced biopsies by 27% while missing 2% of csPCa (compared with biopsy for all).
CONCLUSIONS: Our DBSI-based artificial intelligence model accurately predicted csPCa on biopsy and can be combined with PI-RADS to potentially reduce unnecessary prostate biopsies.
MATERIALS AND METHODS: Between February 2020 and March 2024, 241 patients underwent prostate MRI that included conventional and DBSI-specific sequences before prostate biopsy. We used artificial intelligence models with DBSI metrics as input classifiers and the biopsy pathology as the ground truth. The DBSI-based model was compared with available biomarkers (PSA, PSA density [PSAD], and Prostate Imaging Reporting and Data System [PI-RADS]) for risk discrimination of csPCa defined as Gleason score > 7.
RESULTS: The DBSI-based model was an independent predictor of csPCa (odds ratio [OR] 2.04, 95% CI 1.52-2.73, P < .01), as were PSAD (OR 2.02, 95% CI 1.21-3.35, P = .01) and PI-RADS classification (OR 4.00, 95% CI 1.37-11.6 for PI-RADS 3, P = .01; OR 9.67, 95% CI 2.89-32.7 for PI-RADS 4-5, P < .01), adjusting for age, family history, and race. Within our dataset, the DBSI-based model alone performed similarly to PSAD + PI-RADS (AUC 0.863 vs 0.859, P = .89), while the combination of the DBSI-based model + PI-RADS had the highest risk discrimination for csPCa (AUC 0.894, P < .01). A clinical strategy using the DBSI-based model for patients with PI-RADS 1-3 could have reduced biopsies by 27% while missing 2% of csPCa (compared with biopsy for all).
CONCLUSIONS: Our DBSI-based artificial intelligence model accurately predicted csPCa on biopsy and can be combined with PI-RADS to potentially reduce unnecessary prostate biopsies.