Clinical Validation of Artificial Intelligence–Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection

• Published in: Archives of pathology & laboratory medicine (1976) Vol. 147; no. 10; pp. 1178 - 1185
• Main Authors: Raciti, Patricia, Sue, Jillian, Retamero, Juan A., Ceballos, Rodrigo, Godrich, Ran, Kunz, Jeremy D., Casson, Adam, Thiagarajan, Dilip, Ebrahimzadeh, Zahra, Viret, Julian, Lee, Donghun, Schüffler, Peter J., DeMuth, George, Gulturk, Emre, Kanan, Christopher, Rothrock, Brandon, Reis-Filho, Jorge, Klimstra, David S., Reuter, Victor, Fuchs, Thomas J.
• Format: Journal Article
• Language: English
• Published: United States College of American Pathologists 01.10.2023
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Atrophy; Biological markers; Biopsy; Cancer; Care and treatment; Computer applications; Deep learning; Design; Diagnosis; Digitization; FDA approval; Health aspects; Hyperplasia; Learning algorithms; Machine learning; Medical diagnosis; Medical research; Medicine, Experimental; Methods; Microscopy; Neural networks; Pathology; Patients; Prostate cancer; Prostatic intraepithelial neoplasia; United States; New York; United States > US
• ISSN: 0003-9985; 1543-2165; 1543-2165
• DOI: 10.5858/arpa.2022-0066-OA

Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking. To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance. Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning-based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists' changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads. Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase. This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology.