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Automated tumor analysis for molecular profiling in lung cancer
The discovery and clinical application of molecular biomarkers in solid tumors, increasingly relies on nucleic acid extraction from FFPE tissue sections and subsequent molecular profiling. This in turn requires the pathological review of haematoxylin & eosin (H&E) stained slides, to ensure s...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4695036/ https://www.ncbi.nlm.nih.gov/pubmed/26317646 |
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author | Hamilton, Peter W. Wang, Yinhai Boyd, Clinton James, Jacqueline A. Loughrey, Maurice B. Hougton, Joseph P. Boyle, David P. Kelly, Paul Maxwell, Perry McCleary, David Diamond, James McArt, Darragh G. Tunstall, Jonathon Bankhead, Peter Salto-Tellez, Manuel |
author_facet | Hamilton, Peter W. Wang, Yinhai Boyd, Clinton James, Jacqueline A. Loughrey, Maurice B. Hougton, Joseph P. Boyle, David P. Kelly, Paul Maxwell, Perry McCleary, David Diamond, James McArt, Darragh G. Tunstall, Jonathon Bankhead, Peter Salto-Tellez, Manuel |
author_sort | Hamilton, Peter W. |
collection | PubMed |
description | The discovery and clinical application of molecular biomarkers in solid tumors, increasingly relies on nucleic acid extraction from FFPE tissue sections and subsequent molecular profiling. This in turn requires the pathological review of haematoxylin & eosin (H&E) stained slides, to ensure sample quality, tumor DNA sufficiency by visually estimating the percentage tumor nuclei and tumor annotation for manual macrodissection. In this study on NSCLC, we demonstrate considerable variation in tumor nuclei percentage between pathologists, potentially undermining the precision of NSCLC molecular evaluation and emphasising the need for quantitative tumor evaluation. We subsequently describe the development and validation of a system called TissueMark for automated tumor annotation and percentage tumor nuclei measurement in NSCLC using computerized image analysis. Evaluation of 245 NSCLC slides showed precise automated tumor annotation of cases using Tissuemark, strong concordance with manually drawn boundaries and identical EGFR mutational status, following manual macrodissection from the image analysis generated tumor boundaries. Automated analysis of cell counts for % tumor measurements by Tissuemark showed reduced variability and significant correlation (p < 0.001) with benchmark tumor cell counts. This study demonstrates a robust image analysis technology that can facilitate the automated quantitative analysis of tissue samples for molecular profiling in discovery and diagnostics. |
format | Online Article Text |
id | pubmed-4695036 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-46950362016-01-20 Automated tumor analysis for molecular profiling in lung cancer Hamilton, Peter W. Wang, Yinhai Boyd, Clinton James, Jacqueline A. Loughrey, Maurice B. Hougton, Joseph P. Boyle, David P. Kelly, Paul Maxwell, Perry McCleary, David Diamond, James McArt, Darragh G. Tunstall, Jonathon Bankhead, Peter Salto-Tellez, Manuel Oncotarget Research Paper The discovery and clinical application of molecular biomarkers in solid tumors, increasingly relies on nucleic acid extraction from FFPE tissue sections and subsequent molecular profiling. This in turn requires the pathological review of haematoxylin & eosin (H&E) stained slides, to ensure sample quality, tumor DNA sufficiency by visually estimating the percentage tumor nuclei and tumor annotation for manual macrodissection. In this study on NSCLC, we demonstrate considerable variation in tumor nuclei percentage between pathologists, potentially undermining the precision of NSCLC molecular evaluation and emphasising the need for quantitative tumor evaluation. We subsequently describe the development and validation of a system called TissueMark for automated tumor annotation and percentage tumor nuclei measurement in NSCLC using computerized image analysis. Evaluation of 245 NSCLC slides showed precise automated tumor annotation of cases using Tissuemark, strong concordance with manually drawn boundaries and identical EGFR mutational status, following manual macrodissection from the image analysis generated tumor boundaries. Automated analysis of cell counts for % tumor measurements by Tissuemark showed reduced variability and significant correlation (p < 0.001) with benchmark tumor cell counts. This study demonstrates a robust image analysis technology that can facilitate the automated quantitative analysis of tissue samples for molecular profiling in discovery and diagnostics. Impact Journals LLC 2015-08-03 /pmc/articles/PMC4695036/ /pubmed/26317646 Text en Copyright: © 2015 Hamilton et al. http://creativecommons.org/licenses/by/2.5/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Paper Hamilton, Peter W. Wang, Yinhai Boyd, Clinton James, Jacqueline A. Loughrey, Maurice B. Hougton, Joseph P. Boyle, David P. Kelly, Paul Maxwell, Perry McCleary, David Diamond, James McArt, Darragh G. Tunstall, Jonathon Bankhead, Peter Salto-Tellez, Manuel Automated tumor analysis for molecular profiling in lung cancer |
title | Automated tumor analysis for molecular profiling in lung cancer |
title_full | Automated tumor analysis for molecular profiling in lung cancer |
title_fullStr | Automated tumor analysis for molecular profiling in lung cancer |
title_full_unstemmed | Automated tumor analysis for molecular profiling in lung cancer |
title_short | Automated tumor analysis for molecular profiling in lung cancer |
title_sort | automated tumor analysis for molecular profiling in lung cancer |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4695036/ https://www.ncbi.nlm.nih.gov/pubmed/26317646 |
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