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 W. Gray

doi:10.1109/ISBI.2017.7950717

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 W. Gray

Division of Laboratory and Genomic Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 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 language	English
Title of host publication	2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
Publisher	IEEE Computer Society
Pages	1137-1140
Number of pages	4
ISBN (Electronic)	9781509011711
DOIs	https://doi.org/10.1109/ISBI.2017.7950717
State	Published - Jun 15 2017
Event	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia Duration: Apr 18 2017 → Apr 21 2017

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Country/Territory	Australia
City	Melbourne
Period	04/18/17 → 04/21/17

Keywords

Histopathology
Quantitative Image Analysis

Access to Document

10.1109/ISBI.2017.7950717

Cite this

Azimi, V., Chang, Y. H., Thibault, G., Smith, J., Tsujikawa, T., Kukull, B., Jensen, B., Corless, C., Margolin, A., & Gray, J. W. (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). Article 7950717 (Proceedings - International Symposium on Biomedical Imaging). IEEE Computer Society. https://doi.org/10.1109/ISBI.2017.7950717

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title = "Breast cancer histopathology image analysis pipeline for tumor purity estimation",

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.",

keywords = "Histopathology, Quantitative Image Analysis",

author = "Vahid Azimi and Chang, \{Young Hwan\} and Guillaume Thibault and Jaclyn Smith and Takahiro Tsujikawa and Benjamin Kukull and Bradden Jensen and Christopher Corless and Adam Margolin and Gray, \{Joe W.\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 ; Conference date: 18-04-2017 Through 21-04-2017",

year = "2017",

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doi = "10.1109/ISBI.2017.7950717",

language = "English",

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Azimi, V, Chang, YH, Thibault, G, Smith, J, Tsujikawa, T, Kukull, B, Jensen, B, Corless, C, Margolin, A & Gray, JW 2017, Breast cancer histopathology image analysis pipeline for tumor purity estimation. in 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017., 7950717, Proceedings - International Symposium on Biomedical Imaging, IEEE Computer Society, pp. 1137-1140, 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, Australia, 04/18/17. https://doi.org/10.1109/ISBI.2017.7950717

Breast cancer histopathology image analysis pipeline for tumor purity estimation. / Azimi, Vahid; Chang, Young Hwan; Thibault, Guillaume et al.
2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society, 2017. p. 1137-1140 7950717 (Proceedings - International Symposium on Biomedical Imaging).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Breast cancer histopathology image analysis pipeline for tumor purity estimation

AU - Azimi, Vahid

AU - Chang, Young Hwan

AU - Thibault, Guillaume

AU - Smith, Jaclyn

AU - Tsujikawa, Takahiro

AU - Kukull, Benjamin

AU - Jensen, Bradden

AU - Corless, Christopher

AU - Margolin, Adam

AU - Gray, Joe W.

PY - 2017/6/15

Y1 - 2017/6/15

N2 - 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.

AB - 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.

KW - Histopathology

KW - Quantitative Image Analysis

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DO - 10.1109/ISBI.2017.7950717

M3 - Conference contribution

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T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 1137

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Azimi V, Chang YH, Thibault G, Smith J, Tsujikawa T, Kukull B et al. Breast cancer histopathology image analysis pipeline for tumor purity estimation. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society. 2017. p. 1137-1140. 7950717. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2017.7950717

Breast cancer histopathology image analysis pipeline for tumor purity estimation

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