BACKGROUND: Estrogen receptor (ER) expression is routinely assessed by immunohistochemistry (IHC) in breast carcinoma. Our study compares visual scoring of ER in invasive breast cancer by histopathologists to quantitation of staining using a fully automated system. MATERIALS AND METHODS: A tissue microarray was constructed from 4,049 cases (3,484 included in analysis) of invasive breast carcinoma linked to treatment and outcome information. Slides were scored independently by two pathologists and scores were dichotomised, with ER positivity recognized at a cut-off of >1% positive nuclei. The slides were scanned and analyzed with an Ariol automated system. RESULTS: Using data dichotomised as ER positive or negative, both visual and automated scores were highly consistent: there was excellent concordance between two pathologists (kappa = 0.918 (95%CI: 0.903-0.932)) and between two Ariol machines (kappa = 0.913 (95%CI: 0.897-0.928)). The prognostic significance of ER positivity was similar whether determined by pathologist or automated scoring for both the entire patient cohort and subsets of patients treated with tamoxifen alone or receiving no systemic adjuvant therapy. The optimal cut point for the automated scores using breast cancer disease-specific survival as an endpoint was >0.4% positive nuclei. The concordance between dextran-coated charcoal ER biochemical assay data and automated scores (kappa = 0.728 (95%CI: 0.69-0.75); 0.74 (95%CI: 0.71-0.77)) was similar to the concordance between biochemical assay and pathologist scores (kappa = 0.72 (95%CI: 0.70-0.75; 0.70 (95%CI: 0.67-0.72)). CONCLUSION: Fully automated quantitation of ER immunostaining yields results that do not differ from human scoring against both biochemical assay and patient outcome gold standards.
BACKGROUND:Estrogen receptor (ER) expression is routinely assessed by immunohistochemistry (IHC) in breast carcinoma. Our study compares visual scoring of ER in invasive breast cancer by histopathologists to quantitation of staining using a fully automated system. MATERIALS AND METHODS: A tissue microarray was constructed from 4,049 cases (3,484 included in analysis) of invasive breast carcinoma linked to treatment and outcome information. Slides were scored independently by two pathologists and scores were dichotomised, with ER positivity recognized at a cut-off of >1% positive nuclei. The slides were scanned and analyzed with an Ariol automated system. RESULTS: Using data dichotomised as ER positive or negative, both visual and automated scores were highly consistent: there was excellent concordance between two pathologists (kappa = 0.918 (95%CI: 0.903-0.932)) and between two Ariol machines (kappa = 0.913 (95%CI: 0.897-0.928)). The prognostic significance of ER positivity was similar whether determined by pathologist or automated scoring for both the entire patient cohort and subsets of patients treated with tamoxifen alone or receiving no systemic adjuvant therapy. The optimal cut point for the automated scores using breast cancer disease-specific survival as an endpoint was >0.4% positive nuclei. The concordance between dextran-coated charcoalER biochemical assay data and automated scores (kappa = 0.728 (95%CI: 0.69-0.75); 0.74 (95%CI: 0.71-0.77)) was similar to the concordance between biochemical assay and pathologist scores (kappa = 0.72 (95%CI: 0.70-0.75; 0.70 (95%CI: 0.67-0.72)). CONCLUSION: Fully automated quantitation of ER immunostaining yields results that do not differ from human scoring against both biochemical assay and patient outcome gold standards.
Authors: Julia L-Y Chen; Iñigo Espinosa; Albert Y Lin; Olivia Y-W Liao; Matt van de Rijn; Robert B West Journal: Clin Cancer Res Date: 2013-06-26 Impact factor: 12.531
Authors: Torsten O Nielsen; Joel S Parker; Samuel Leung; David Voduc; Mark Ebbert; Tammi Vickery; Sherri R Davies; Jacqueline Snider; Inge J Stijleman; Jerry Reed; Maggie C U Cheang; Elaine R Mardis; Charles M Perou; Philip S Bernard; Matthew J Ellis Journal: Clin Cancer Res Date: 2010-09-13 Impact factor: 12.531
Authors: László Krecsák; Tamás Micsik; Gábor Kiszler; Tibor Krenács; Dániel Szabó; Viktor Jónás; Gergely Császár; László Czuni; Péter Gurzó; Levente Ficsor; Béla Molnár Journal: Diagn Pathol Date: 2011-01-18 Impact factor: 2.644
Authors: Yue Sun; Dmitry A Turbin; Kun Ling; Narendra Thapa; Samuel Leung; David G Huntsman; Richard A Anderson Journal: Breast Cancer Res Date: 2010-01-14 Impact factor: 6.466