ArticleLaboratory investigation; a journal of technical methods and pathology2017
Whole tumor section quantitative image analysis maximizes between-pathologists' reproducibility for clinical immunohistochemistry-based biomarkers.
Article in Laboratory investigation; a journal of technical methods and pathology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- The Cost of Accessibility: Considerations for Adopting Microscope Camera-Derived Images for Digital and Computational Pathology.Mayo Clinic proceedings. Digital health · 2026Review
- Intra-slide calibration technology improves immunohistochemical harmonization within and between anatomic pathology laboratories.bioRxiv : the preprint server for biology · 2026Article
- Novel approach to HER2 quantification using phosphor-integrated dots in human breast invasive cancer microarray.PloS one · 2024Article
- Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Article
- Atlas of PD-L1 for Pathologists: Indications, Scores, Diagnostic Platforms and Reporting Systems.Journal of personalized medicine · 2022Review
- Digital quantitative tissue image analysis of hypoxia in resected pancreatic ductal adenocarcinomas.Frontiers in oncology · 2022Article
- Assessment of estrogen receptor low positive status in breast cancer: Implications for pathologists and oncologists.Histology and histopathology · 2021Review
- Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study.Virchows Archiv : an international journal of pathology · 2021Article
- Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.Applied immunohistochemistry & molecular morphology : AIMM · 2021Article
- Best Practices for Technical Reproducibility Assessment of Multiplex Immunofluorescence.Frontiers in molecular biosciences · 2021Review
- Digital Slide Assessment for Programmed Death-Ligand 1 Combined Positive Score in Head and Neck Squamous Carcinoma: Focus on Validation and Vision.Frontiers in artificial intelligence · 2021Article
- Article
- Development and applications of computer image analysis algorithms for scoring of PD-L1 immunohistochemistry.Immuno-oncology technology · 2020Review
- Quantitative digital imaging analysis of HER2 immunohistochemistry predicts the response to anti-HER2 neoadjuvant chemotherapy in HER2-positive breast carcinoma.Breast cancer research and treatment · 2020Article
- Multiplexed Immunohistochemistry for Molecular and Immune Profiling in Lung Cancer-Just About Ready for Prime-Time?Cancers · 2019Review
- Intertumoral Heterogeneity of CD3Clinical cancer research : an official journal of the American Association for Cancer Research · 2019Article
- Systems pathology by multiplexed immunohistochemistry and whole-slide digital image analysis.Scientific reports · 2017Article
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Authors and funding
14 authors.
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Abstract
Pathologists have had increasing responsibility for quantitating immunohistochemistry (IHC) biomarkers with the expectation of high between-reader reproducibility due to clinical decision-making especially for patient therapy. Digital imaging-based quantitation of IHC clinical slides offers a potential aid for improvement; however, its clinical adoption is limited potentially due to a conventional field-of-view annotation approach. In this study, we implemented a novel solely morphology-based whole tumor section annotation strategy to maximize image analysis quantitation results between readers. We first compare the field-of-view image analysis annotation approach to digital and manual-based modalities across multiple clinical studies (~120 cases per study) and biomarkers (ER, PR, HER2, Ki-67, and p53 IHC) and then compare a subset of the same cases (~40 cases each from the ER, PR, HER2, and Ki-67 studies) using whole tumor section annotation approach to understand incremental value of all modalities. Between-reader results for each biomarker in relation to conventional scoring modalities showed similar concordance as manual read: ER field-of-view image analysis: 95.3% (95% CI 92.0-98.2%) vs digital read: 92.0% (87.8-95.8%) vs manual read: 94.9% (91.4-97.8%); PR field-of-view image analysis: 94.1% (90.3-97.2%) vs digital read: 94.0% (90.2-97.1%) vs manual read: 94.4% (90.9-97.2%); Ki-67 field-of-view image analysis: 86.8% (82.1-91.4%) vs digital read: 76.6% (70.9-82.2%) vs manual read: 85.6% (80.4-90.4%); p53 field-of-view image analysis: 81.7% (76.4-86.8%) vs digital read: 80.6% (75.0-86.0%) vs manual read: 78.8% (72.2-83.3%); and HER2 field-of-view image analysis: 93.8% (90.0-97.2%) vs digital read: 91.0 (86.6-94.9%) vs manual read: 87.2% (82.1-91.9%). Subset implementation and analysis on the same cases using whole tumor section image analysis approach showed significant improvement between pathologists over field-of-view image analysis and manual read (HER2 100% (97-100%), P=0.013 field-of-view image analysis and 0.013 manual read; Ki-67 100% (96.9-100%), P=0.040 and 0.012; ER 98.3% (94.1-99.5%), p=0.232 and 0.181; and PR 96.6% (91.5-98.7%), p=0.012 and 0.257). Overall, whole tumor section image analysis significantly improves between-pathologist's reproducibility and is the optimal approach for clinical-based image analysis algorithms.
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