Computer-Aided Quantitative Image Analysis of Multiple Human Epidermal Growth Factor Receptor 2 Immunohistochemistry Assays in Breast Carcinoma.
Context.--Quantitative image analysis (QIA) is increasingly applied for immunohistochemistry (IHC)-based biomarker assessment in pathology. With the introduction of drugs targeting breast carcinomas (BCs) with low human epidermal growth factor receptor 2 (HER2) expression levels, a need for more acc...
| Published in: | Archives of Pathology & Laboratory Medicine Vol. 150; no. 8; pp. 610 - 620 |
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| Main Authors: | , , , , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
College of American Pathologists
Aug2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196173101&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196173101 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Aug2026 vid: 150 iid: 8 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 196173101 196173101 196173101 10.5858/arpa.2025-0316-OA 196173101 ppf: 610 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Computer-Aided Quantitative Image Analysis of Multiple Human Epidermal Growth Factor Receptor 2 Immunohistochemistry Assays in Breast Carcinoma. aug: au: Cossutta, Mélissande Truumees, Birgit Papine, Alexandre Kristoffersen, Heidi Lykke Tatarinova, Ekaterina Emanuelsen, Lise Homsy, Charles Soussaline, Françoise Nielsen, Søren affil: Research and Development Department, IMSTAR Dx, Paris, France sug: subj: Breast Neoplasms Pathology Epidermal Growth Factor Receptors Immunohistochemistry Image Processing, Computer Assisted Reproducibility of Results Human Pathologists Comparative Studies Validation Studies Staining and Labeling Sensitivity and Specificity Confidence Intervals Descriptive Statistics Tumor Markers, Biological ab: Context.--Quantitative image analysis (QIA) is increasingly applied for immunohistochemistry (IHC)-based biomarker assessment in pathology. With the introduction of drugs targeting breast carcinomas (BCs) with low human epidermal growth factor receptor 2 (HER2) expression levels, a need for more accurate and reproducible HER2 scoring is warranted. QIA has the potential to increase HER2 IHC scoring reproducibility. However, QIA reproducibility should be agnostic and uninfluenced by different IHC assays. Objective.--To compare HER2 scoring accuracy and reproducibility by expert pathologists and QIA in BCs analyzed with different IHC assays. Design.--Algorithm parameters of the QIA method were developed by using 183 slides from 6 Nordic immunohistochemical Quality Control (NordiQC) HER2 runs. The QIA method was then validated on 409 slides from 2 other runs. HER2 scores obtained by visual scoring, following 2023 European Society for Medical Oncology and American Society of Clinical Oncology/College of American Pathologists guidelines or by QIA, were compared. Results.--Visual scoring and QIA reached 86% (κ = 0.81) overall scoring agreement for tissue cores in the final validation material. QIA showed 98% (95% CI, 97.8%-99.0%) sensitivity and 84% (95% CI, 80.3%-87.6%) specificity for the distinction between HER2 0 (no drug candidate) and HER2 1-31 (drug candidates) scores. Ninety-nine percent (95% CI, 98.7%-99.9%) sensitivity and 100% (95% CI, 99.8%-100%) specificity were obtained for the separation between HER2 2-31 in situ hybridization (ISH)-positive cores (HER2 classical overexpression) and HER2 0-21 ISH-negative cores (HER2-low expression range). Conclusions.--Our QIA method can be beneficial to use as a reliable HER2 scoring tool for HER2 classical overexpression and HER2-low in BCs. It was found to be agnostic and provided same accuracy irrespective of IHC assay applied. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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