| Autor: |
Marletta, Stefano, Eccher, Albino, Martelli, Filippo Maria, Santonicco, Nicola, Girolami, Ilaria, Scarpa, Aldo, Pagni, Fabio, L'Imperio, Vincenzo, Pantanowitz, Liron, Gobbo, Stefano, Seminati, Davide, Tos, Angelo Paolo Dei, Parwani, Anil |
| Předmět: |
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| Zdroj: |
American Journal of Clinical Pathology; Jun2024, Vol. 161 Issue 6, p526-534, 9p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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