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作 者:Vittorio Agosti Enrico Munari
机构地区:[1]Section of Pathology,Department of Molecular and Translational Medicine,University of Brescia,Brescia 25121,Italy [2]Department of Pathology and Diagnostics,University and Hospital Trust of Verona,Verona 37126,Italy
出 处:《Asian Journal of Andrology》2024年第6期575-581,共7页亚洲男性学杂志(英文版)
摘 要:A crucial aspect of prostate cancer grading, especially in low- and intermediate-risk cancer, is the accurate identification of Gleason pattern 4 glands, which includes ill-formed or fused glands. However, there is notable inconsistency among pathologists in recognizing these glands, especially when mixed with pattern 3 glands. This inconsistency has significant implications for patient management and treatment decisions. Conversely, the recognition of glomeruloid and cribriform architecture has shown higher reproducibility. Cribriform architecture, in particular, has been linked to the worst prognosis among pattern 4 subtypes. Intraductal carcinoma of the prostate (IDC-P) is also associated with high-grade cancer and poor prognosis. Accurate identification, classification, and tumor size evaluation by pathologists are vital for determining patient treatment. This review emphasizes the importance of prostate cancer grading, highlighting challenges like distinguishing between pattern 3 and pattern 4 and the prognostic implications of cribriform architecture and intraductal proliferations. It also addresses the inherent grading limitations due to interobserver variability and explores the potential of computational pathology to enhance pathologist accuracy and consistency.
关 键 词:artificial intelligence cancer CRIBRIFORM GRADING INTRADUCTAL PROSTATE
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