%0 Systematic Review
%T Artificial intelligence-based algorithms for the diagnosis of prostate cancer: A systematic review.
%A Marletta S
%A Eccher A
%A Martelli FM
%A Santonicco N
%A Girolami I
%A Scarpa A
%A Pagni F
%A L'Imperio V
%A Pantanowitz L
%A Gobbo S
%A Seminati D
%A Dei Tos AP
%A Parwani A
%J Am J Clin Pathol
%V 161
%N 6
%D 2024 Jun 3
%M 38381582
%F 5.4
%R 10.1093/ajcp/aqad182
%X <B>OBJECTIVE: </B>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.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>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.