Performance of Deep Learning and Genitourinary Radiologists in Detection of Prostate Cancer Using 3‐T Multiparametric Magnetic Resonance Imaging. Issue 2 (12th March 2021)

• Record Type: Journal Article
• Authors: Cao, Ruiming; Zhong, Xinran; Afshari, Sohrab; Felker, Ely; Suvannarerg, Voraparee; Tubtawee, Teeravut; Vangala, Sitaram; Scalzo, Fabien; Raman, Steven; Sung, Kyunghyun
• Is Part Of: Journal of magnetic resonance imaging. Volume 54:Issue 2(2021)
• Page Start: 474
• Page End: 483
• Publication Date: 2021-03-12
• Subjects: deep learning -- prostate cancer -- automatic cancer detection -- multiparametric MRI; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27595
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Several deep learning‐based techniques have been developed for prostate cancer (PCa) detection using multiparametric magnetic resonance imaging (mpMRI), but few of them have been rigorously evaluated relative to radiologists' performance or whole‐mount histopathology (WMHP). Purpose: To compare the performance of a previously proposed deep learning algorithm, FocalNet, and expert radiologists in the detection of PCa on mpMRI with WMHP as the reference. Study Type: Retrospective, single‐center study. Subjects: A total of 553 patients (development cohort: 427 patients; evaluation cohort: 126 patients) who underwent 3‐T mpMRI prior to radical prostatectomy from October 2010 to February 2018. Field Strength/Sequence: 3‐T, T2‐weighted imaging and diffusion‐weighted imaging. Assessment: FocalNet was trained on the development cohort to predict PCa locations by detection points, with a confidence value for each point, on the evaluation cohort. Four fellowship‐trained genitourinary (GU) radiologists independently evaluated the evaluation cohort to detect suspicious PCa foci, annotate detection point locations, and assign a five‐point suspicion score (1: least suspicious, 5: most suspicious) for each annotated detection point. The PCa detection performance of FocalNet and radiologists were evaluated by the lesion detection sensitivity vs. the number of false‐positive detections at different thresholds on suspicion scores. Clinically significant lesions: Gleason Group (GG) ≥ 2 or pathological size ≥ 10 mm. Index lesions: the highest GG and the largest pathological size (secondary). Statistical Tests: Bootstrap hypothesis test for the detection sensitivity between radiologists and FocalNet. Results: For the overall differential detection sensitivity, FocalNet was 5.1% and 4.7% below the radiologists for clinically significant and index lesions, respectively; however, the differences were not statistically significant ( P = 0.413 and P = 0.282, respectively). Data Conclusion: FocalNet achieved slightly lower but not statistically significant PCa detection performance compared with GU radiologists. Compared with radiologists, FocalNet demonstrated similar detection performance for a highly sensitive setting (suspicion score ≥ 1) or a highly specific setting (suspicion score = 5), while lower performance in between. Level of Evidence: 3 Technical Efficacy Stage: 2