{"title":"乳腺癌中人类表皮生长因子受体 2 免疫组化评分的全自动人工智能解决方案：多阅读器研究。","authors":"Savitri Krishnamurthy, Stuart J Schnitt, Anne Vincent-Salomon, Rita Canas-Marques, Eugenia Colon, Kanchan Kantekure, Marina Maklakovski, Wilfrid Finck, Jeanne Thomassin, Yuval Globerson, Lilach Bien, Giuseppe Mallel, Maya Grinwald, Chaim Linhart, Judith Sandbank, Manuela Vecsler","doi":"10.1200/PO.24.00353","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>The proven efficacy of human epidermal growth factor receptor 2 (HER2) antibody-drug conjugate therapy for treating HER2-low breast cancers necessitates more accurate and reproducible HER2 immunohistochemistry (IHC) scoring. We aimed to validate performance and utility of a fully automated artificial intelligence (AI) solution for interpreting HER2 IHC in breast carcinoma.</p><p><strong>Materials and methods: </strong>A two-arm multireader study of 120 HER2 IHC whole-slide images from four sites assessed HER2 scoring by four surgical pathologists without and with the aid of an AI HER2 solution. Both arms were compared with high-confidence ground truth (GT) established by agreement of at least four of five breast pathology subspecialists according to ASCO/College of American Pathologists (CAP) 2018/2023 guidelines.</p><p><strong>Results: </strong>The mean interobserver agreement among GT pathologists across all HER2 scores was 72.4% (N = 120). The AI solution demonstrated high accuracy for HER2 scoring, with 92.1% agreement on slides with high confidence GT (n = 92). The use of the AI tool led to improved performance by readers, interobserver agreement increased from 75.0% for digital manual read to 83.7% for AI-assisted review, and scoring accuracy improved from 85.3% to 88.0%. For the distinction of HER2 0 from 1+ cases (n = 58), pathologists supported by AI showed significantly higher interobserver agreement (69.8% without AI <i>v</i> 87.4% with AI) and accuracy (81.9% without AI <i>v</i> 88.8% with AI).</p><p><strong>Conclusion: </strong>This study demonstrated utility of a fully automated AI solution to aid in scoring HER2 IHC accurately according to ASCO/CAP 2018/2023 guidelines. Pathologists supported by AI showed improvements in HER2 IHC scoring consistency and accuracy, especially for distinguishing HER2 0 from 1+ cases. This AI solution could be used by pathologists as a decision support tool for enhancing reproducibility and consistency of HER2 scoring and particularly for identifying HER2-low breast cancers.</p>","PeriodicalId":14797,"journal":{"name":"JCO precision oncology","volume":"8 ","pages":"e2400353"},"PeriodicalIF":5.3000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11485213/pdf/","citationCount":"0","resultStr":"{\"title\":\"Fully Automated Artificial Intelligence Solution for Human Epidermal Growth Factor Receptor 2 Immunohistochemistry Scoring in Breast Cancer: A Multireader Study.\",\"authors\":\"Savitri Krishnamurthy, Stuart J Schnitt, Anne Vincent-Salomon, Rita Canas-Marques, Eugenia Colon, Kanchan Kantekure, Marina Maklakovski, Wilfrid Finck, Jeanne Thomassin, Yuval Globerson, Lilach Bien, Giuseppe Mallel, Maya Grinwald, Chaim Linhart, Judith Sandbank, Manuela Vecsler\",\"doi\":\"10.1200/PO.24.00353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>The proven efficacy of human epidermal growth factor receptor 2 (HER2) antibody-drug conjugate therapy for treating HER2-low breast cancers necessitates more accurate and reproducible HER2 immunohistochemistry (IHC) scoring. We aimed to validate performance and utility of a fully automated artificial intelligence (AI) solution for interpreting HER2 IHC in breast carcinoma.</p><p><strong>Materials and methods: </strong>A two-arm multireader study of 120 HER2 IHC whole-slide images from four sites assessed HER2 scoring by four surgical pathologists without and with the aid of an AI HER2 solution. Both arms were compared with high-confidence ground truth (GT) established by agreement of at least four of five breast pathology subspecialists according to ASCO/College of American Pathologists (CAP) 2018/2023 guidelines.</p><p><strong>Results: </strong>The mean interobserver agreement among GT pathologists across all HER2 scores was 72.4% (N = 120). The AI solution demonstrated high accuracy for HER2 scoring, with 92.1% agreement on slides with high confidence GT (n = 92). The use of the AI tool led to improved performance by readers, interobserver agreement increased from 75.0% for digital manual read to 83.7% for AI-assisted review, and scoring accuracy improved from 85.3% to 88.0%. For the distinction of HER2 0 from 1+ cases (n = 58), pathologists supported by AI showed significantly higher interobserver agreement (69.8% without AI <i>v</i> 87.4% with AI) and accuracy (81.9% without AI <i>v</i> 88.8% with AI).</p><p><strong>Conclusion: </strong>This study demonstrated utility of a fully automated AI solution to aid in scoring HER2 IHC accurately according to ASCO/CAP 2018/2023 guidelines. Pathologists supported by AI showed improvements in HER2 IHC scoring consistency and accuracy, especially for distinguishing HER2 0 from 1+ cases. This AI solution could be used by pathologists as a decision support tool for enhancing reproducibility and consistency of HER2 scoring and particularly for identifying HER2-low breast cancers.</p>\",\"PeriodicalId\":14797,\"journal\":{\"name\":\"JCO precision oncology\",\"volume\":\"8 \",\"pages\":\"e2400353\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11485213/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JCO precision oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1200/PO.24.00353\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/11 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JCO precision oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1200/PO.24.00353","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/11 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Fully Automated Artificial Intelligence Solution for Human Epidermal Growth Factor Receptor 2 Immunohistochemistry Scoring in Breast Cancer: A Multireader Study.
Purpose: The proven efficacy of human epidermal growth factor receptor 2 (HER2) antibody-drug conjugate therapy for treating HER2-low breast cancers necessitates more accurate and reproducible HER2 immunohistochemistry (IHC) scoring. We aimed to validate performance and utility of a fully automated artificial intelligence (AI) solution for interpreting HER2 IHC in breast carcinoma.
Materials and methods: A two-arm multireader study of 120 HER2 IHC whole-slide images from four sites assessed HER2 scoring by four surgical pathologists without and with the aid of an AI HER2 solution. Both arms were compared with high-confidence ground truth (GT) established by agreement of at least four of five breast pathology subspecialists according to ASCO/College of American Pathologists (CAP) 2018/2023 guidelines.
Results: The mean interobserver agreement among GT pathologists across all HER2 scores was 72.4% (N = 120). The AI solution demonstrated high accuracy for HER2 scoring, with 92.1% agreement on slides with high confidence GT (n = 92). The use of the AI tool led to improved performance by readers, interobserver agreement increased from 75.0% for digital manual read to 83.7% for AI-assisted review, and scoring accuracy improved from 85.3% to 88.0%. For the distinction of HER2 0 from 1+ cases (n = 58), pathologists supported by AI showed significantly higher interobserver agreement (69.8% without AI v 87.4% with AI) and accuracy (81.9% without AI v 88.8% with AI).
Conclusion: This study demonstrated utility of a fully automated AI solution to aid in scoring HER2 IHC accurately according to ASCO/CAP 2018/2023 guidelines. Pathologists supported by AI showed improvements in HER2 IHC scoring consistency and accuracy, especially for distinguishing HER2 0 from 1+ cases. This AI solution could be used by pathologists as a decision support tool for enhancing reproducibility and consistency of HER2 scoring and particularly for identifying HER2-low breast cancers.
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