人工智能的临床验证增强病理学诊断在前列腺癌症检测中显示出显著的诊断准确性提高。

IF 3.7 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY Archives of pathology & laboratory medicine Pub Date : 2023-10-01 DOI:10.5858/arpa.2022-0066-OA
Patricia Raciti, Jillian Sue, Juan A Retamero, Rodrigo Ceballos, Ran Godrich, Jeremy D Kunz, Adam Casson, Dilip Thiagarajan, Zahra Ebrahimzadeh, Julian Viret, Donghun Lee, Peter J Schüffler, George DeMuth, Emre Gulturk, Christopher Kanan, Brandon Rothrock, Jorge Reis-Filho, David S Klimstra, Victor Reuter, Thomas J Fuchs
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引用次数: 11

摘要

上下文。--:前列腺癌症的诊断取决于病理学家对组织的准确评估。人工智能(AI)在数字化整张幻灯片图像(WSI)中的应用可以帮助病理学家诊断癌症,但在模拟临床环境中缺乏可靠、多样的证据。目标。--:比较病理学家在人工智能辅助和非人工智能辅助下阅读前列腺活检标本WSI的诊断准确性。设计。--:18名病理学家,其中2名是泌尿生殖亚专科医生,评估了218家机构准备的610份前列腺针芯活检WSI,并可选择延期。对每个WSI依次进行两次评估:最初没有辅助,随后立即由Paige Prostate(PaPr)辅助,这是一个基于深度学习的系统,提供了癌症或良性的可疑WSI二元分类,并在可疑WSI上精确定位了最有可能携带癌症的位置。评估了病理学家在辅助和非辅助模式之间的敏感性和特异性变化,以及PaPr输出对辅助读数的影响。结果。--:使用PaPr,病理学家提高了他们在所有组织学分级和肿瘤大小方面的敏感性和特异性。良性和癌性WSI的准确性提高可归因于PaPr,它正确地对100%的WSI进行了分类,显示在PaPr辅助阶段的正确诊断。结论。--:这项研究证明了病理学家在模拟诊断实践中使用人工智能工具的有效性和安全性,弥合了计算病理学研究与其临床应用之间的差距,并导致美国食品药品监督管理局首次授权病理学中的人工智能系统。
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Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection.

Context.—: Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking.

Objective.—: To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance.

Design.—: Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning-based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists' changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads.

Results.—: Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase.

Conclusions.—: This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology.

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来源期刊
CiteScore
9.20
自引率
2.20%
发文量
369
审稿时长
3-8 weeks
期刊介绍: Welcome to the website of the Archives of Pathology & Laboratory Medicine (APLM). This monthly, peer-reviewed journal of the College of American Pathologists offers global reach and highest measured readership among pathology journals. Published since 1926, ARCHIVES was voted in 2009 the only pathology journal among the top 100 most influential journals of the past 100 years by the BioMedical and Life Sciences Division of the Special Libraries Association. Online access to the full-text and PDF files of APLM articles is free.
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