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
{"title":"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。","authors":"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","doi":"10.5858/arpa.2022-0066-OA","DOIUrl":null,"url":null,"abstract":"<p><strong>Context.—: </strong>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.</p><p><strong>Objective.—: </strong>To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance.</p><p><strong>Design.—: </strong>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.</p><p><strong>Results.—: </strong>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.</p><p><strong>Conclusions.—: </strong>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.</p>","PeriodicalId":8305,"journal":{"name":"Archives of pathology & laboratory medicine","volume":" ","pages":"1178-1185"},"PeriodicalIF":3.7000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection.\",\"authors\":\"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\",\"doi\":\"10.5858/arpa.2022-0066-OA\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Context.—: </strong>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.</p><p><strong>Objective.—: </strong>To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance.</p><p><strong>Design.—: </strong>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.</p><p><strong>Results.—: </strong>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.</p><p><strong>Conclusions.—: </strong>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.</p>\",\"PeriodicalId\":8305,\"journal\":{\"name\":\"Archives of pathology & laboratory medicine\",\"volume\":\" \",\"pages\":\"1178-1185\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of pathology & laboratory medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.5858/arpa.2022-0066-OA\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL LABORATORY TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of pathology & laboratory medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5858/arpa.2022-0066-OA","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
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.
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.