Google Health, Palo Alto, California.
Google Health via Advanced Clinical, Deerfield, Illinois.
JAMA Netw Open. 2020 Nov 2;3(11):e2023267. doi: 10.1001/jamanetworkopen.2020.23267.
Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored.
To evaluate an expert-level AI-based assistive tool when used by pathologists for the grading of prostate biopsies.
DESIGN, SETTING, AND PARTICIPANTS: This diagnostic study used a fully crossed multiple-reader, multiple-case design to evaluate an AI-based assistive tool for prostate biopsy grading. Retrospective grading of prostate core needle biopsies from 2 independent medical laboratories in the US was performed between October 2019 and January 2020. A total of 20 general pathologists reviewed 240 prostate core needle biopsies from 240 patients. Each pathologist was randomized to 1 of 2 study cohorts. The 2 cohorts reviewed every case in the opposite modality (with AI assistance vs without AI assistance) to each other, with the modality switching after every 10 cases. After a minimum 4-week washout period for each batch, the pathologists reviewed the cases for a second time using the opposite modality. The pathologist-provided grade group for each biopsy was compared with the majority opinion of urologic pathology subspecialists.
An AI-based assistive tool for Gleason grading of prostate biopsies.
Agreement between pathologists and subspecialists with and without the use of an AI-based assistive tool for the grading of all prostate biopsies and Gleason grade group 1 biopsies.
Biopsies from 240 patients (median age, 67 years; range, 39-91 years) with a median prostate-specific antigen level of 6.5 ng/mL (range, 0.6-97.0 ng/mL) were included in the analyses. Artificial intelligence-assisted review by pathologists was associated with a 5.6% increase (95% CI, 3.2%-7.9%; P < .001) in agreement with subspecialists (from 69.7% for unassisted reviews to 75.3% for assisted reviews) across all biopsies and a 6.2% increase (95% CI, 2.7%-9.8%; P = .001) in agreement with subspecialists (from 72.3% for unassisted reviews to 78.5% for assisted reviews) for grade group 1 biopsies. A secondary analysis indicated that AI assistance was also associated with improvements in tumor detection, mean review time, mean self-reported confidence, and interpathologist agreement.
In this study, the use of an AI-based assistive tool for the review of prostate biopsies was associated with improvements in the quality, efficiency, and consistency of cancer detection and grading.
最近已经开发出用于前列腺活检分级的专家级人工智能（AI）算法。然而，将此类算法集成到病理学家工作流程中的潜在影响在很大程度上仍未得到探索。
评估病理学家使用基于人工智能的辅助工具进行前列腺活检分级的效果。
设计、设置和参与者：本项诊断研究采用完全交叉的多位读者、多位病例设计，评估一种基于人工智能的辅助工具在前列腺活检分级中的应用。在 2019 年 10 月至 2020 年 1 月期间，对来自美国 2 个独立医学实验室的前列腺核心针活检进行了回顾性分级。共有 20 名普通病理学家对来自 240 名患者的 240 个前列腺核心针活检进行了评估。每位病理学家随机分配到 2 个研究队列中的 1 个。这 2 个队列相互交替使用 AI 辅助或不使用 AI 辅助进行评估，每完成 10 例后切换模式。在每批病例完成后至少 4 周的洗脱期后，病理学家再次使用相反的模式对病例进行第二次评估。将每位病理学家提供的每个活检分级与泌尿科病理专家的多数意见进行比较。
用于前列腺活检 Gleason 分级的基于人工智能的辅助工具。
使用和不使用基于人工智能的辅助工具评估所有前列腺活检和 Gleason 分级 1 活检时病理学家和专家之间的一致性。
对来自 240 名患者（中位年龄 67 岁；范围 39-91 岁）的活检进行了分析，这些患者的中位前列腺特异性抗原水平为 6.5 ng/mL（范围 0.6-97.0 ng/mL）。在所有活检中，使用 AI 辅助的病理学家评估与专家的一致性提高了 5.6%（95%CI，3.2%-7.9%；P < .001）（从无辅助评估的 69.7%提高到有辅助评估的 75.3%），在 Gleason 分级 1 活检中提高了 6.2%（95%CI，2.7%-9.8%；P = .001）（从无辅助评估的 72.3%提高到有辅助评估的 78.5%）。一项次要分析表明，人工智能辅助还可以提高肿瘤检测的准确性、平均审查时间、平均自我报告的信心和病理学家之间的一致性。
在这项研究中，使用基于人工智能的辅助工具对前列腺活检进行评估可提高癌症检测和分级的质量、效率和一致性。
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