A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study. Issue 10 (October 2021)

• Record Type: Journal Article
• Authors: Winkel, David J.; Tong, Angela; Lou, Bin; Kamen, Ali; Comaniciu, Dorin; Disselhorst, Jonathan A.; Rodríguez-Ruiz, Alejandro; Huisman, Henkjan; Szolar, Dieter; Shabunin, Ivan; Choi, Moon Hyung; Xing, Pengyi; Penzkofer, Tobias; Grimm, Robert; von Busch, Heinrich; Boll, Daniel T.
• Is Part Of: Investigative radiology. Volume 56:Issue 10(2021)
• Publication Date: 2021-10
• Subjects: prostatic neoplasms -- deep learning -- computer-aided diagnosis -- magnetic resonance imaging; Diagnosis, Radioscopic -- Periodicals; Radiology, Medical -- Periodicals; 616.0757
• DOI: 10.1097/RLI.0000000000000780
• Languages: English
• ISSNs: 0020-9996

Abstract

Abstract : Objective: The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans. Materials and Methods: We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1–5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss κ), and average reading time without DL-CAD assistance were evaluated. Results: The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS ≥4) was 0.84 (95% confidence interval [CI], 0.79–0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83–0.94) with an improvement of 4.4% (95% CI, 1.1%–7.7%; P = 0.010). Interreader concordance (in terms of Fleiss κ) increased from 0.22 to 0.36 ( P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS ≥3 was improved by 2.9% ( P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds ( P < 0.001). Conclusions: Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time.