Independent real‐world application of a clinical‐grade automated prostate cancer detection system. Issue 2 (27th April 2021)

• Record Type: Journal Article
• Authors: da Silva, Leonard M; Pereira, Emilio M; Salles, Paulo GO; Godrich, Ran; Ceballos, Rodrigo; Kunz, Jeremy D; Casson, Adam; Viret, Julian; Chandarlapaty, Sarat; Ferreira, Carlos Gil; Ferrari, Bruno; Rothrock, Brandon; Raciti, Patricia; Reuter, Victor; Dogdas, Belma; DeMuth, George; Sue, Jillian; Kanan, Christopher; Grady, Leo; Fuchs, Thomas J; Reis‐Filho, Jorge S
• Is Part Of: Journal of pathology. Volume 254:Issue 2(2021)
• Page Start: 147
• Page End: 158
• Publication Date: 2021-04-27
• Subjects: artificial intelligence -- histopathology -- diagnosis -- screening -- prostate cancer -- diagnosis -- deep learning -- machine learning; Pathology -- Periodicals; 616.07
• DOI: 10.1002/path.5662
• Languages: English
• ISSNs: 0022-3417

Abstract

Abstract: Artificial intelligence (AI)‐based systems applied to histopathology whole‐slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists. We sought to define the performance of an AI‐based automated prostate cancer detection system, Paige Prostate, when applied to independent real‐world data. The algorithm was employed to classify slides into two categories: benign (no further review needed) or suspicious (additional histologic and/or immunohistochemical analysis required). We assessed the sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) of a local pathologist, two central pathologists, and Paige Prostate in the diagnosis of 600 transrectal ultrasound‐guided prostate needle core biopsy regions ('part‐specimens') from 100 consecutive patients, and to ascertain the impact of Paige Prostate on diagnostic accuracy and efficiency. Paige Prostate displayed high sensitivity (0.99; CI 0.96–1.0), NPV (1.0; CI 0.98–1.0), and specificity (0.93; CI 0.90–0.96) at the part‐specimen level. At the patient level, Paige Prostate displayed optimal sensitivity (1.0; CI 0.93–1.0) and NPV (1.0; CI 0.91–1.0) at a specificity of 0.78 (CI 0.64–0.89). The 27 part‐specimens considered by Paige Prostate as suspicious, whose final diagnosis was benign, were found to comprise atrophy ( n = 14), atrophy and apical prostate tissue ( n = 1), apical/benign prostate tissue ( n = 9), adenosis ( n = 2), and post‐atrophic hyperplasia ( n = 1). Paige Prostate resulted in the identification of four additional patients whose diagnoses were upgraded from benign/suspicious to malignant. Additionally, this AI‐based test provided an estimated 65.5% reduction of the diagnostic time for the material analyzed. Given its optimal sensitivity and NPV, Paige Prostate has the potential to be employed for the automated identification of patients whose histologic slides could forgo full histopathologic review. In addition to providing incremental improvements in diagnostic accuracy and efficiency, this AI‐based system identified patients whose prostate cancers were not initially diagnosed by three experienced histopathologists. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.