Rachel K Vanderschelden, Jacob A Jerome, Daniel Gonzalez, Lindsey Seigh, Gloria J Carter, Beth Z Clark, Esther Elishaev, Jeffrey Louis Fine, Lakshmi Harinath, Mirka W Jones, Tatiana M Villatoro, Thing Rinda Soong, Jing Yu, Chengquan Zhao, Doug Hartman, Rohit Bhargava
{"title":"Implementation of Digital Image Analysis in Assessment of Ki67 Index in Breast Cancer.","authors":"Rachel K Vanderschelden, Jacob A Jerome, Daniel Gonzalez, Lindsey Seigh, Gloria J Carter, Beth Z Clark, Esther Elishaev, Jeffrey Louis Fine, Lakshmi Harinath, Mirka W Jones, Tatiana M Villatoro, Thing Rinda Soong, Jing Yu, Chengquan Zhao, Doug Hartman, Rohit Bhargava","doi":"10.1097/PAI.0000000000001171","DOIUrl":null,"url":null,"abstract":"<p><p>The clinical utility of the proliferation marker Ki67 in breast cancer treatment and prognosis is an active area of research. Studies have suggested that differences in pre-analytic and analytic factors contribute to low analytical validity of the assay, with scoring methods accounting for a large proportion of this variability. Use of standard scoring methods is limited, in part due to the time intensive nature of such reporting protocols. Therefore, use of digital image analysis tools may help to both standardize reporting and improve workflow. In this study, digital image analysis was utilized to quantify Ki67 indices in 280 breast biopsy and resection specimens during routine clinical practice. The supervised Ki67 indices were then assessed for agreement with a manual count of 500 tumor cells. Agreement was excellent, with an intraclass correlation coefficient of 0.96 for the pathologist-supervised analysis. This study illustrates an example of a rapid, accurate workflow for implementation of digital image analysis in Ki67 scoring in breast cancer.</p>","PeriodicalId":48952,"journal":{"name":"Applied Immunohistochemistry & Molecular Morphology","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Immunohistochemistry & Molecular Morphology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/PAI.0000000000001171","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/11/6 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ANATOMY & MORPHOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The clinical utility of the proliferation marker Ki67 in breast cancer treatment and prognosis is an active area of research. Studies have suggested that differences in pre-analytic and analytic factors contribute to low analytical validity of the assay, with scoring methods accounting for a large proportion of this variability. Use of standard scoring methods is limited, in part due to the time intensive nature of such reporting protocols. Therefore, use of digital image analysis tools may help to both standardize reporting and improve workflow. In this study, digital image analysis was utilized to quantify Ki67 indices in 280 breast biopsy and resection specimens during routine clinical practice. The supervised Ki67 indices were then assessed for agreement with a manual count of 500 tumor cells. Agreement was excellent, with an intraclass correlation coefficient of 0.96 for the pathologist-supervised analysis. This study illustrates an example of a rapid, accurate workflow for implementation of digital image analysis in Ki67 scoring in breast cancer.
期刊介绍:
Applied Immunohistochemistry & Molecular Morphology covers newly developed identification and detection technologies, and their applications in research and diagnosis for the applied immunohistochemist & molecular Morphologist.
Official Journal of the International Society for Immunohistochemisty and Molecular Morphology.