Automated tumor analysis for molecular profiling in lung cancer
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Peter W. Hamilton1,4,* Yinhai Wang1,4,*, Clinton Boyd2, Jacqueline A. James1, Maurice B. Loughrey3,1, Joseph P. Hougton3, David P. Boyle1, Paul Kelly3, Perry Maxwell1, David McCleary3, James Diamond3, Darragh G. McArt1, Jonathon Tunstall3, Peter Bankhead1, Manuel Salto-Tellez1,3
1Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, UK
2Department of Cellular and Molecular Pathology, Antrim Area Hospital, Antrim, UK
3Institute of Pathology, Royal Victoria Hospital, Belfast, UK
4PathXL Ltd, Northern Ireland Science Park, Belfast, UK
*These authors have contributed equally to this work
Peter W. Hamilton, e-mail: firstname.lastname@example.org
Keywords: molecular pathology, manual macrodissection, percentage tumor, image analysis, digital pathology
Received: April 09, 2015 Accepted: July 24, 2015 Published: August 03, 2015
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.
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