{"title":"人工智能在乳腺病理学中的应用。","authors":"Yueping Liu, Dandan Han, Anil V Parwani, Zaibo Li","doi":"10.5858/arpa.2022-0457-RA","DOIUrl":null,"url":null,"abstract":"<p><strong>Context.—: </strong>Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology.</p><p><strong>Objective.—: </strong>To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes.</p><p><strong>Data sources.—: </strong>We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience.</p><p><strong>Conclusions.—: </strong>With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.</p>","PeriodicalId":8305,"journal":{"name":"Archives of pathology & laboratory medicine","volume":"147 9","pages":"1003-1013"},"PeriodicalIF":3.7000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Applications of Artificial Intelligence in Breast Pathology.\",\"authors\":\"Yueping Liu, Dandan Han, Anil V Parwani, Zaibo Li\",\"doi\":\"10.5858/arpa.2022-0457-RA\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Context.—: </strong>Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology.</p><p><strong>Objective.—: </strong>To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes.</p><p><strong>Data sources.—: </strong>We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience.</p><p><strong>Conclusions.—: </strong>With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.</p>\",\"PeriodicalId\":8305,\"journal\":{\"name\":\"Archives of pathology & laboratory medicine\",\"volume\":\"147 9\",\"pages\":\"1003-1013\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of pathology & laboratory medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.5858/arpa.2022-0457-RA\",\"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.2022-0457-RA","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
Applications of Artificial Intelligence in Breast Pathology.
Context.—: Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology.
Objective.—: To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes.
Data sources.—: We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience.
Conclusions.—: With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.
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.