Deep‐Learning‐Based Artificial Intelligence for PI‐RADS Classification to Assist Multiparametric Prostate MRI Interpretation: A Development Study

• Published in: Journal of magnetic resonance imaging Vol. 52; no. 5; pp. 1499 - 1507
• Main Authors: Sanford, Thomas, Harmon, Stephanie A., Turkbey, Evrim B., Kesani, Deepak, Tuncer, Sena, Madariaga, Manuel, Yang, Chris, Sackett, Jonathan, Mehralivand, Sherif, Yan, Pingkun, Xu, Sheng, Wood, Bradford J., Merino, Maria J., Pinto, Peter A., Choyke, Peter L., Turkbey, Baris
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.11.2020; Wiley Subscription Services, Inc
• Subjects: Artificial Intelligence; Artificial neural networks; Biopsy; Cancer; Classification; Deep Learning; Diffusion coefficient; Field strength; Humans; Lesions; Magnetic Resonance Imaging; Male; Medical imaging; MRI; Multiparametric Magnetic Resonance Imaging; Neural networks; PI‐RADS; Prostate; Prostatic Neoplasms - diagnostic imaging; Retrospective Studies; Statistical analysis; Statistical tests; deep learning; prostate; MRI; PI-RADS; artificial intelligence
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.27204

Background The Prostate Imaging Reporting and Data System (PI‐RADS) provides guidelines for risk stratification of lesions detected on multiparametric MRI (mpMRI) of the prostate but suffers from high intra/interreader variability. Purpose To develop an artificial intelligence (AI) solution for PI‐RADS classification and compare its performance with an expert radiologist using targeted biopsy results. Study Type Retrospective study including data from our institution and the publicly available ProstateX dataset. Population In all, 687 patients who underwent mpMRI of the prostate and had one or more detectable lesions (PI‐RADS score >1) according to PI‐RADSv2. Field Strength/Sequence T2‐weighted, diffusion‐weighted imaging (DWI; five evenly spaced b values between b = 0–750 s/mm2) for apparent diffusion coefficient (ADC) mapping, high b‐value DWI (b = 1500 or 2000 s/mm2), and dynamic contrast‐enhanced T1‐weighted series were obtained at 3.0T. Assessment PI‐RADS lesions were segmented by a radiologist. Bounding boxes around the T2/ADC/high‐b value segmentations were stacked and saved as JPEGs. These images were used to train a convolutional neural network (CNN). The PI‐RADS scores obtained by the CNN were compared with radiologist scores. The cancer detection rate was measured from a subset of patients who underwent biopsy. Statistical Tests Agreement between the AI and the radiologist‐driven PI‐RADS scores was assessed using a kappa score, and differences between categorical variables were assessed with a Wald test. Results For the 1034 detection lesions, the kappa score for the AI system vs. the expert radiologist was moderate, at 0.40. However, there was no significant difference in the rates of detection of clinically significant cancer for any PI‐RADS score in 86 patients undergoing targeted biopsy (P = 0.4–0.6). Data Conclusion We developed an AI system for assignment of a PI‐RADS score on segmented lesions on mpMRI with moderate agreement with an expert radiologist and a similar ability to detect clinically significant cancer. Level of Evidence 4 Technical Efficacy Stage 2