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

BACKGROUND: The immunohistochemical detection of estrogen (ER) and progesterone (PR) receptors in breast cancer is routinely used for prognostic and predictive testing. Whole slide digitalization supported by dedicated software tools allows quantization of the image objects (e.g. cell membrane, nucl...

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Autores principales: Krecsák, László, Micsik, Tamás, Kiszler, Gábor, Krenács, Tibor, Szabó, Dániel, Jónás, Viktor, Császár, Gergely, Czuni, László, Gurzó, Péter, Ficsor, Levente, Molnár, Béla
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3031194/
https://www.ncbi.nlm.nih.gov/pubmed/21244664
http://dx.doi.org/10.1186/1746-1596-6-6
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author Krecsák, László
Micsik, Tamás
Kiszler, Gábor
Krenács, Tibor
Szabó, Dániel
Jónás, Viktor
Császár, Gergely
Czuni, László
Gurzó, Péter
Ficsor, Levente
Molnár, Béla
author_facet Krecsák, László
Micsik, Tamás
Kiszler, Gábor
Krenács, Tibor
Szabó, Dániel
Jónás, Viktor
Császár, Gergely
Czuni, László
Gurzó, Péter
Ficsor, Levente
Molnár, Béla
author_sort Krecsák, László
collection PubMed
description BACKGROUND: The immunohistochemical detection of estrogen (ER) and progesterone (PR) receptors in breast cancer is routinely used for prognostic and predictive testing. Whole slide digitalization supported by dedicated software tools allows quantization of the image objects (e.g. cell membrane, nuclei) and an unbiased analysis of immunostaining results. Validation studies of image analysis applications for the detection of ER and PR in breast cancer specimens provided strong concordance between the pathologist's manual assessment of slides and scoring performed using different software applications. METHODS: The effectiveness of two connected semi-automated image analysis software (NuclearQuant v. 1.13 application for Pannoramic™ Viewer v. 1.14) for determination of ER and PR status in formalin-fixed paraffin embedded breast cancer specimens immunostained with the automated Leica Bond Max system was studied. First the detection algorithm was calibrated to the scores provided an independent assessors (pathologist), using selected areas from 38 small digital slides (created from 16 cases) containing a mean number of 195 cells. Each cell was manually marked and scored according to the Allred-system combining frequency and intensity scores. The performance of the calibrated algorithm was tested on 16 cases (14 invasive ductal carcinoma, 2 invasive lobular carcinoma) against the pathologist's manual scoring of digital slides. RESULTS: The detection was calibrated to 87 percent object detection agreement and almost perfect Total Score agreement (Cohen's kappa 0.859, quadratic weighted kappa 0.986) from slight or moderate agreement at the start of the study, using the un-calibrated algorithm. The performance of the application was tested against the pathologist's manual scoring of digital slides on 53 regions of interest of 16 ER and PR slides covering all positivity ranges, and the quadratic weighted kappa provided almost perfect agreement (κ = 0.981) among the two scoring schemes. CONCLUSIONS: NuclearQuant v. 1.13 application for Pannoramic™ Viewer v. 1.14 software application proved to be a reliable image analysis tool for pathologists testing ER and PR status in breast cancer.
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spelling pubmed-30311942011-02-01 Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer Krecsák, László Micsik, Tamás Kiszler, Gábor Krenács, Tibor Szabó, Dániel Jónás, Viktor Császár, Gergely Czuni, László Gurzó, Péter Ficsor, Levente Molnár, Béla Diagn Pathol Research BACKGROUND: The immunohistochemical detection of estrogen (ER) and progesterone (PR) receptors in breast cancer is routinely used for prognostic and predictive testing. Whole slide digitalization supported by dedicated software tools allows quantization of the image objects (e.g. cell membrane, nuclei) and an unbiased analysis of immunostaining results. Validation studies of image analysis applications for the detection of ER and PR in breast cancer specimens provided strong concordance between the pathologist's manual assessment of slides and scoring performed using different software applications. METHODS: The effectiveness of two connected semi-automated image analysis software (NuclearQuant v. 1.13 application for Pannoramic™ Viewer v. 1.14) for determination of ER and PR status in formalin-fixed paraffin embedded breast cancer specimens immunostained with the automated Leica Bond Max system was studied. First the detection algorithm was calibrated to the scores provided an independent assessors (pathologist), using selected areas from 38 small digital slides (created from 16 cases) containing a mean number of 195 cells. Each cell was manually marked and scored according to the Allred-system combining frequency and intensity scores. The performance of the calibrated algorithm was tested on 16 cases (14 invasive ductal carcinoma, 2 invasive lobular carcinoma) against the pathologist's manual scoring of digital slides. RESULTS: The detection was calibrated to 87 percent object detection agreement and almost perfect Total Score agreement (Cohen's kappa 0.859, quadratic weighted kappa 0.986) from slight or moderate agreement at the start of the study, using the un-calibrated algorithm. The performance of the application was tested against the pathologist's manual scoring of digital slides on 53 regions of interest of 16 ER and PR slides covering all positivity ranges, and the quadratic weighted kappa provided almost perfect agreement (κ = 0.981) among the two scoring schemes. CONCLUSIONS: NuclearQuant v. 1.13 application for Pannoramic™ Viewer v. 1.14 software application proved to be a reliable image analysis tool for pathologists testing ER and PR status in breast cancer. BioMed Central 2011-01-18 /pmc/articles/PMC3031194/ /pubmed/21244664 http://dx.doi.org/10.1186/1746-1596-6-6 Text en Copyright ©2011 Krecsák et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Krecsák, László
Micsik, Tamás
Kiszler, Gábor
Krenács, Tibor
Szabó, Dániel
Jónás, Viktor
Császár, Gergely
Czuni, László
Gurzó, Péter
Ficsor, Levente
Molnár, Béla
Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer
title Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer
title_full Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer
title_fullStr Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer
title_full_unstemmed Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer
title_short Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer
title_sort technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3031194/
https://www.ncbi.nlm.nih.gov/pubmed/21244664
http://dx.doi.org/10.1186/1746-1596-6-6
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