Literature DB >> 15129906

Interobserver agreement for estrogen receptor immunohistochemical analysis in breast cancer: a comparison of manual and computer-assisted scoring methods.

Leslie K Diaz1, Aysegul Sahin, Nour Sneige.   

Abstract

Differences in scoring methods for estrogen receptor (ER) immunohistochemistry may cause significant variation in the results. Scoring practices differ within the United States and internationally and include semiquantitative scoring formulas, manual estimations, and computer-assisted techniques. The goal of this study was to determine the rate of interobserver variability for manual ER scoring at our institution and compare the ER scores obtained by manual scoring with those obtained using image-analysis software (QCA, Lake Bluff, IL). In a series of 70 consecutive invasive breast cancers, ER was assayed using standard immunohistochemical techniques and the monoclonal antibody 6F11. Scoring was performed independently by three breast pathologists, and the scores were compared with those obtained using the QCA image-analysis system, using 10% nuclear staining as the cutoff for positivity. We found that 43 cases (61%) were ER positive, 25 cases (36%) were ER negative, and two cases (3%) showed ER staining of less than 10%. The consensus scores for the 70 cases showed a high level of agreement with the ER scores determined by image analysis (kappa = 0.84). Interobserver variability was low. The kappa scores for each observer showed strong agreement with the consensus score, the image-analysis score, and between the observers. Our findings show that interobserver agreement for manual scoring of ER is strong, and that manual or computer-aided scoring techniques are comparable.

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Year:  2004        PMID: 15129906     DOI: 10.1016/j.anndiagpath.2003.11.004

Source DB:  PubMed          Journal:  Ann Diagn Pathol        ISSN: 1092-9134            Impact factor:   2.090


  13 in total

1.  Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research.

Authors:  Spiros Kostopoulos; Panagiota Ravazoula; Pantelis Asvestas; Ioannis Kalatzis; George Xenogiannopoulos; Dionisis Cavouras; Dimitris Glotsos
Journal:  J Digit Imaging       Date:  2017-06       Impact factor: 4.056

2.  Comparison of evaluations of hormone receptors in breast carcinoma by image-analysis using three automated immunohistochemical stainings.

Authors:  Koji Arihiro; Miyo Oda; Katsunari Ogawa; Kenshi Tominaga; Yoshie Kaneko; Tomomi Shimizu; Shiho Ohnishi; Megumi Oda; Yuki Kurita; Yuko Taira; Masayoshi Fujii; Maiko Tanaka
Journal:  Exp Ther Med       Date:  2010-08-26       Impact factor: 2.447

3.  Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer.

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

4.  Assessment of automated image analysis of breast cancer tissue microarrays for epidemiologic studies.

Authors:  Kelly L Bolton; Montserrat Garcia-Closas; Ruth M Pfeiffer; Máire A Duggan; William J Howat; Stephen M Hewitt; Xiaohong R Yang; Robert Cornelison; Sarah L Anzick; Paul Meltzer; Sean Davis; Petra Lenz; Jonine D Figueroa; Paul D P Pharoah; Mark E Sherman
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-03-23       Impact factor: 4.254

5.  Using image analysis as a tool for assessment of prognostic and predictive biomarkers for breast cancer: How reliable is it?

Authors:  Mark C Lloyd; Pushpa Allam-Nandyala; Chetna N Purohit; Nancy Burke; Domenico Coppola; Marilyn M Bui
Journal:  J Pathol Inform       Date:  2010-12-23

6.  Distinct distribution and prognostic significance of molecular subtypes of breast cancer in Chinese women: a population-based cohort study.

Authors:  Yinghao Su; Ying Zheng; Wei Zheng; Kai Gu; Zhi Chen; Guoliang Li; Qiuyin Cai; Wei Lu; Xiao Ou Shu
Journal:  BMC Cancer       Date:  2011-07-12       Impact factor: 4.430

7.  Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.

Authors:  Haydee Lara; Zaibo Li; Esther Abels; Famke Aeffner; Marilyn M Bui; Ehab A ElGabry; Cleopatra Kozlowski; Michael C Montalto; Anil V Parwani; Mark D Zarella; Douglas Bowman; David Rimm; Liron Pantanowitz
Journal:  Appl Immunohistochem Mol Morphol       Date:  2021-08-01

8.  Reliability of a computational platform as a surrogate for manually interpreted immunohistochemical markers in breast tumor tissue microarrays.

Authors:  Michelle R Roberts; Gabrielle M Baker; Yujing J Heng; Michael E Pyle; Kristina Astone; Bernard A Rosner; Laura C Collins; A Heather Eliassen; Rulla M Tamimi
Journal:  Cancer Epidemiol       Date:  2021-08-02       Impact factor: 2.890

9.  Novel image analysis approach for quantifying expression of nuclear proteins assessed by immunohistochemistry: application to measurement of oestrogen and progesterone receptor levels in breast cancer.

Authors:  Elton Rexhepaj; Donal J Brennan; Peter Holloway; Elaine W Kay; Amanda H McCann; Goran Landberg; Michael J Duffy; Karin Jirstrom; William M Gallagher
Journal:  Breast Cancer Res       Date:  2008-10-23       Impact factor: 6.466

10.  Cytoplasmic location of factor-inhibiting hypoxia-inducible factor is associated with an enhanced hypoxic response and a shorter survival in invasive breast cancer.

Authors:  Ern Yu Tan; Leticia Campo; Cheng Han; Helen Turley; Francesco Pezzella; Kevin C Gatter; Adrian L Harris; Stephen B Fox
Journal:  Breast Cancer Res       Date:  2007       Impact factor: 6.466

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