Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience. Issue 142 (September 2021)

• Record Type: Journal Article
• Authors: Youn, Seo Yeon; Choi, Moon Hyung; Kim, Dong Hwan; Lee, Young Joon; Huisman, Henkjan; Johnson, Evan; Penzkofer, Tobias; Shabunin, Ivan; Winkel, David Jean; Xing, Pengyi; Szolar, Dieter; Grimm, Robert; von Busch, Heinrich; Son, Yohan; Lou, Bin; Kamen, Ali
• Is Part Of: European journal of radiology. Issue 142(2021)
• Publication Date: 2021-09
• Subjects: Deep learning -- Prostate -- Prostate neoplasms -- Prostate imaging reporting and data system -- Magnetic resonance imaging; AI artificial intelligence -- bpMRI biparametric MRI -- CSC clinically significant prostate cancer -- DLA deep learning-based algorithm -- mpMRI multiparametric MRI -- PI-RADS Prostate Imaging-Reporting and Data System -- PSA prostate-specific antigen -- TRUS transrectal ultrasonography; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.109894
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Deep learning-based algorithm (DLA) can promisingly assign PI-RADS categories. PI-RADS categories assigned by the radiologists with different experience level varied. The sensitivities and specificities of the DLA and expert were similar with PI-RADS ≥ 4. The performance of DLA was similar to that of clinical reports in clinical practice. Abstract: Purpose: To compare the performance of lesion detection and Prostate Imaging-Reporting and Data System (PI-RADS) classification between a deep learning-based algorithm (DLA), clinical reports and radiologists with different levels of experience in prostate MRI. Methods: This retrospective study included 121 patients who underwent prebiopsy MRI and prostate biopsy. More than five radiologists (Reader groups 1, 2: residents; Readers 3, 4: less-experienced radiologists; Reader 5: expert) independently reviewed biparametric MRI (bpMRI). The DLA results were obtained using bpMRI. The reference standard was based on pathologic reports. The diagnostic performance of the PI-RADS classification of DLA, clinical reports, and radiologists was analyzed using AUROC. Dichotomous analysis (PI-RADS cutoff value ≥ 3 or 4) was performed, and the sensitivities and specificities were compared using McNemar's test. Results: Clinically significant cancer [CSC, Gleason score ≥ 7] was confirmed in 43 patients (35.5%). The AUROC of the DLA (0.828) for diagnosing CSC was significantly higher than that of Reader 1 (AUROC, 0.706; p = 0.011), significantly lower than that of Reader 5 (AUROC, 0.914; p = 0.013), and similar to clinical reports and other readers (p = 0.060–0.661). The sensitivity of DLA (76.7%) was comparable to those of all readers and the clinical reports at a PI-RADS cutoff value ≥ 4. The specificity of the DLA (85.9%) was significantly higher than those of clinical reports and Readers 2–3 and comparable to all others at a PI-RADS cutoff value ≥ 4. Conclusions: The DLA showed moderate diagnostic performance at a level between those of residents and an expert in detecting and classifying according to PI-RADS. The performance of DLA was similar to that of clinical reports from various radiologists in clinical practice.