A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading.

Artificial intelligence (AI) is a promising tool in pathology, including cancer diagnosis, subtyping, grading, and prognostic prediction.

The aim of the study is to assess AI application in prostate cancer (PCa) histology. We carried out a systematic literature search in 3 databases. Primary outcome was AI accuracy in differentiating between PCa and benign hyperplasia. Secondary outcomes were AI accuracy in determining Gleason grade and agreement among AI and pathologists.

Our final sample consists of 24 studies conducted from 2007 to 2021. They aggregate data from roughly 8000 cases of prostate biopsy and 458 cases of radical prostatectomy (RP). Sensitivity for PCa diagnostic exceeded 90% and ranged from 87% to 100%, and specificity varied from 68% to 99%. Overall accuracy ranged from 83.7% to 98.3% with AUC reaching 0.99. The meta-analysis using the Mantel-Haenszel method showed pooled sensitivity of 0.96 with I2 = 80.7% and pooled specificity of 0.95 with I2 = 86.1%. Pooled positive likehood ratio was 15.3 with I2 = 87.3% and negative - was 0.04 with I2 = 78.6%. SROC (symmetric receiver operating characteristics) curve represents AUC = 0.99. For grading the accuracy of AI was lower: sensitivity for Gleason grading ranged from 77% to 87%, and specificity from 82% to 90%.

The accuracy of AI for PCa identification and grading is comparable to expert pathologists. This is a promising approach which has several possible clinical applications resulting in expedite and optimize pathology reports. AI introduction into common practice may be limited by difficult and time-consuming convolutional neural network training and tuning.

Prostate cancer and prostatic diseases. 2023 Apr 25 [Epub ahead of print]

Andrey Morozov, Mark Taratkin, Andrey Bazarkin, Juan Gomez Rivas, Stefano Puliatti, Enrico Checcucci, Ines Rivero Belenchon, Karl-Friedrich Kowalewski, Anastasia Shpikina, Nirmish Singla, Jeremy Y C Teoh, Vasiliy Kozlov, Severin Rodler, Pietro Piazza, Harun Fajkovic, Maxim Yakimov, Andre Luis Abreu, Giovanni E Cacciamani, Dmitry Enikeev, Young Academic Urologists (YAU) Working Group in Uro-technology of the European Association of Urology

Institute for Urology and Reproductive Health, Sechenov University, Moscow, Russia., Institute for Clinical Medicine, Sechenov University, Moscow, Russia., Department of Urology, Clinico San Carlos University Hospital, Madrid, Spain., Urology Department, University of Modena and Reggio Emilia, Modena, Italy., Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy., Department of Uro-Nephrology. Virgen del Rocío University Hospital. Seville, "Seville Biomedicine Institute, IBiS/ Virgen del Rocío University Hospital /CSIC/Seville University. Seville", Seville, Spain., Department of Urology, University Medical Center Mannheim, Heidelberg University, Heidelberg, Germany., Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, USA., Department of Surgery, S.H. Ho Urology Centre, The Chinese University of Hong Kong, Hong Kong, China., Department of Public Health and Healthcare, Sechenov University, Moscow, Russia., Department of Urology, Ludwig-Maximilian-University Munich, Munich, Germany., Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy., Karl Landsteiner Institute of Urology and Andrology, Vienna, Austria., Pathology department, Rabin Medical Center, Petach Tikwa, Israel., USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, Los Angeles, CA, USA., Institute for Urology and Reproductive Health, Sechenov University, Moscow, Russia. .