Anikó Kovács, Leif Klint, Barbro Linderholm, Toshima Z Parris
{"title":"通过常规光镜、数字病理学和人工智能评估47例晚期乳腺癌核心活检、匹配手术标本中HER2low和her2 -超低状态的变化及其远处转移。","authors":"Anikó Kovács, Leif Klint, Barbro Linderholm, Toshima Z Parris","doi":"10.1007/s10549-025-07776-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>HER2-targeted therapies have improved survival in HER2-positive breast cancer, and recent data suggest potential benefits for patients with HER2-low tumors (defined as immunohistochemistry (IHC) 1 + or 2 + and, in situ hybridization (ISH)-negative). HER2-low tumors are heterogenous, spanning the hormone receptor-positive and triple-negative subtypes. Assessing HER2-low and HER2-ultralow status remains challenging, especially across specimen types.</p><p><strong>Aims: </strong>This study aims to (1) compare HER2 assessment using conventional microscopy, digital pathology, and an artificial intelligence (AI) model, and (2) investigate changes in HER2-low status between core biopsies, surgical specimens, and metastases.</p><p><strong>Materials and methods: </strong>IHC slides from 47 HER2-low advanced breast carcinomas were analyzed using conventional microscopy, digital pathology, and an AI model developed on Aiforia® Create. HER2 statuses were categorized as low, ultralow (score 1 + in 1-10%), and null (score 0 or 1 + in < 1% with difficult-to-interpret minimal membranous-like staining). Changes in HER2 expression across specimen types were evaluated using agreement measures and visualization tools.</p><p><strong>Results: </strong>The AI model identified more HER2-low and HER2-ultralow cases compared to conventional methods, improving detection accuracy. HER2 expression differed between specimen types, with metastases exhibiting increased HER2 expression compared to surgical specimens and core biopsies. Digital pathology also showed stronger membranous staining and identified more HER2 expressor tumor cells with any kind of membranous staining than microscopy.</p><p><strong>Conclusions: </strong>AI evaluation is a more sensitive method for HER2-low assessment and reveals expression changes across disease progression. These findings emphasize the need for standardized HER2 assessment to ensure accurate therapy eligibility, particularly for novel treatments like Trastuzumab-Deruxtecan.</p>","PeriodicalId":9133,"journal":{"name":"Breast Cancer Research and Treatment","volume":" ","pages":"397-408"},"PeriodicalIF":3.0000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396990/pdf/","citationCount":"0","resultStr":"{\"title\":\"Changes in HER2low and HER2-ultralow status in 47 advanced breast carcinoma core biopsies, matching surgical specimens, and their distant metastases assessed by conventional light microscopy, digital pathology, and artificial intelligence.\",\"authors\":\"Anikó Kovács, Leif Klint, Barbro Linderholm, Toshima Z Parris\",\"doi\":\"10.1007/s10549-025-07776-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>HER2-targeted therapies have improved survival in HER2-positive breast cancer, and recent data suggest potential benefits for patients with HER2-low tumors (defined as immunohistochemistry (IHC) 1 + or 2 + and, in situ hybridization (ISH)-negative). HER2-low tumors are heterogenous, spanning the hormone receptor-positive and triple-negative subtypes. Assessing HER2-low and HER2-ultralow status remains challenging, especially across specimen types.</p><p><strong>Aims: </strong>This study aims to (1) compare HER2 assessment using conventional microscopy, digital pathology, and an artificial intelligence (AI) model, and (2) investigate changes in HER2-low status between core biopsies, surgical specimens, and metastases.</p><p><strong>Materials and methods: </strong>IHC slides from 47 HER2-low advanced breast carcinomas were analyzed using conventional microscopy, digital pathology, and an AI model developed on Aiforia® Create. HER2 statuses were categorized as low, ultralow (score 1 + in 1-10%), and null (score 0 or 1 + in < 1% with difficult-to-interpret minimal membranous-like staining). Changes in HER2 expression across specimen types were evaluated using agreement measures and visualization tools.</p><p><strong>Results: </strong>The AI model identified more HER2-low and HER2-ultralow cases compared to conventional methods, improving detection accuracy. HER2 expression differed between specimen types, with metastases exhibiting increased HER2 expression compared to surgical specimens and core biopsies. Digital pathology also showed stronger membranous staining and identified more HER2 expressor tumor cells with any kind of membranous staining than microscopy.</p><p><strong>Conclusions: </strong>AI evaluation is a more sensitive method for HER2-low assessment and reveals expression changes across disease progression. These findings emphasize the need for standardized HER2 assessment to ensure accurate therapy eligibility, particularly for novel treatments like Trastuzumab-Deruxtecan.</p>\",\"PeriodicalId\":9133,\"journal\":{\"name\":\"Breast Cancer Research and Treatment\",\"volume\":\" \",\"pages\":\"397-408\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396990/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Breast Cancer Research and Treatment\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10549-025-07776-6\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/7/22 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Breast Cancer Research and Treatment","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10549-025-07776-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/7/22 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Changes in HER2low and HER2-ultralow status in 47 advanced breast carcinoma core biopsies, matching surgical specimens, and their distant metastases assessed by conventional light microscopy, digital pathology, and artificial intelligence.
Background: HER2-targeted therapies have improved survival in HER2-positive breast cancer, and recent data suggest potential benefits for patients with HER2-low tumors (defined as immunohistochemistry (IHC) 1 + or 2 + and, in situ hybridization (ISH)-negative). HER2-low tumors are heterogenous, spanning the hormone receptor-positive and triple-negative subtypes. Assessing HER2-low and HER2-ultralow status remains challenging, especially across specimen types.
Aims: This study aims to (1) compare HER2 assessment using conventional microscopy, digital pathology, and an artificial intelligence (AI) model, and (2) investigate changes in HER2-low status between core biopsies, surgical specimens, and metastases.
Materials and methods: IHC slides from 47 HER2-low advanced breast carcinomas were analyzed using conventional microscopy, digital pathology, and an AI model developed on Aiforia® Create. HER2 statuses were categorized as low, ultralow (score 1 + in 1-10%), and null (score 0 or 1 + in < 1% with difficult-to-interpret minimal membranous-like staining). Changes in HER2 expression across specimen types were evaluated using agreement measures and visualization tools.
Results: The AI model identified more HER2-low and HER2-ultralow cases compared to conventional methods, improving detection accuracy. HER2 expression differed between specimen types, with metastases exhibiting increased HER2 expression compared to surgical specimens and core biopsies. Digital pathology also showed stronger membranous staining and identified more HER2 expressor tumor cells with any kind of membranous staining than microscopy.
Conclusions: AI evaluation is a more sensitive method for HER2-low assessment and reveals expression changes across disease progression. These findings emphasize the need for standardized HER2 assessment to ensure accurate therapy eligibility, particularly for novel treatments like Trastuzumab-Deruxtecan.
期刊介绍:
Breast Cancer Research and Treatment provides the surgeon, radiotherapist, medical oncologist, endocrinologist, epidemiologist, immunologist or cell biologist investigating problems in breast cancer a single forum for communication. The journal creates a "market place" for breast cancer topics which cuts across all the usual lines of disciplines, providing a site for presenting pertinent investigations, and for discussing critical questions relevant to the entire field. It seeks to develop a new focus and new perspectives for all those concerned with breast cancer.