Artificial intelligence–based algorithms for the diagnosis of prostate cancer: A systematic review

• Published in: American journal of clinical pathology Vol. 161; no. 6; pp. 526 - 534
• Main Authors: Marletta, Stefano, Eccher, Albino, Martelli, Filippo Maria, Santonicco, Nicola, Girolami, Ilaria, Scarpa, Aldo, Pagni, Fabio, L’Imperio, Vincenzo, Pantanowitz, Liron, Gobbo, Stefano, Seminati, Davide, Dei Tos, Angelo Paolo, Parwani, Anil
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 03.06.2024
• Subjects: Algorithms; Artificial Intelligence; Cancer; Diagnosis; Health aspects; Humans; Learning strategies; Male; Oncology, Experimental; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - pathology; diagnosis; prostate cancer; systematic review; digital pathology; convolutional neural network; artificial intelligence
• ISSN: 0002-9173; 1943-7722; 1943-7722
• DOI: 10.1093/ajcp/aqad182

Abstract Objectives The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging (WSI) and artificial intelligence (AI) have both gained approval for primary diagnosis in prostate pathology, providing physicians with novel tools for their daily routine. Methods A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was carried out in electronic databases to gather the available evidence on the application of AI-based algorithms to prostate cancer. Results Of 6290 articles, 80 were included, mostly (59%) dealing with biopsy specimens. Glass slides were digitized to WSI in most studies (89%), roughly two-thirds of which (66%) exploited convolutional neural networks for computational analysis. The algorithms achieved good to excellent results about cancer detection and grading, along with significantly reduced TATs. Furthermore, several studies showed a relevant correlation between AI-identified histologic features and prognostic predictive variables such as biochemical recurrence, extraprostatic extension, perineural invasion, and disease-free survival. Conclusions The published evidence suggests that AI can be reliably used for prostate cancer detection and grading, assisting pathologists in the time-consuming screening of slides. Further technologic improvement would help widening AI’s adoption in prostate pathology, as well as expanding its prognostic predictive potential.