Applications of artificial intelligence in prostate cancer histopathology

被引：6

作者：

Busby, Dallin ^{[1
]}

Grauer, Ralph ^{[1
]}

Pandav, Krunal ^{[1
]}

Khosla, Akshita ^{[2
]}

Jain, Parag ^{[3
]}

Menon, Mani ^{[1
]}

Haines III, G. Kenneth ^{[1
]}

Cordon-Cardo, Carlos ^{[4
]}

Gorin, Michael A. ^{[1
]}

Tewari, Ashutosh K. ^{[1
]}

机构：

[1] Icahn Sch Med Mt Sinai, Dept Urol, New York, NY 10029 USA

[2] Crozer Chester Med Ctr, Dept Internal Med, Philadelphia, PA USA

[3] PathomIQ Inc, Cupertino, CA USA

[4] Icahn Sch Med Mt Sinai, Dept Pathol, New York, NY USA

来源：

UROLOGIC ONCOLOGY-SEMINARS AND ORIGINAL INVESTIGATIONS | 2024年 / 42卷 / 03期

关键词：

Prostate cancer; Histopathology; Artificial intelligence; Machine learning; Deep learning; Gleason grading; WHOLE-SLIDE IMAGES; BIOPSIES; PREDICTION; RECURRENCE; DIAGNOSIS;

D O I：

10.1016/j.urolonc.2022.12.002

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

The diagnosis of prostate cancer (PCa) depends on the evaluation of core needle biopsies by trained pathologists. Artificial intelligence (AI) derived models have been created to address the challenges posed by pathologists' increasing workload, workforce shortages, and variability in histopathology assessment. These models with histopathological parameters integrated into sophisticated neural networks demonstrate remarkable ability to identify, grade, and predict outcomes for PCa. Though the fully autonomous diagnosis of PCa remains elusive, recently published data suggests that AI has begun to serve as an initial screening tool, an assistant in the form of a real -time interactive interface during histological analysis, and as a second read system to detect false negative diagnoses. Our article aims to describe recent advances and future opportunities for AI in PCa histopathology. (c) 2022 Published by Elsevier Inc.

引用

页码：37 / 47

页数：11

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