Breast cancer histopathology image analysis pipeline for tumor purity estimation

Vahid Azimi, Young Hwan Chang, Guillaume Thibault, Jaclyn Smith, Takahiro Tsujikawa, Benjamin Kukull, Bradden Jensen, Christopher Corless, Adam Margolin, Joe Gray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The translation of genomic sequencing technology to the clinic has greatly advanced personalized medicine. However, the presence of normal cells in tumors is a confounding factor in genome sequence analysis. Tumor purity, or the percentage of cancerous cells in whole tissue section, is a correction factor that can be used to improve the clinical utility of genomic sequencing. Currently, tumor purity is estimated visually by expert pathologists; however, it has been shown that there exist vast inter-observer discrepancies in tumor purity scoring. In this paper, we propose a quantitative image analysis pipeline for tumor purity estimation and provide a systematic comparison between pathologists' scores and our image-based tumor purity estimation.

Original languageEnglish (US)
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE Computer Society
Pages1137-1140
Number of pages4
ISBN (Electronic)9781509011711
DOIs
StatePublished - Jun 15 2017
Event14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia
Duration: Apr 18 2017Apr 21 2017

Other

Other14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
CountryAustralia
CityMelbourne
Period4/18/174/21/17

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Keywords

  • Histopathology
  • Quantitative Image Analysis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Azimi, V., Chang, Y. H., Thibault, G., Smith, J., Tsujikawa, T., Kukull, B., Jensen, B., Corless, C., Margolin, A., & Gray, J. (2017). Breast cancer histopathology image analysis pipeline for tumor purity estimation. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 (pp. 1137-1140). [7950717] IEEE Computer Society. https://doi.org/10.1109/ISBI.2017.7950717