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.}",

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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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AU - Azimi, Vahid

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AU - Tsujikawa, Takahiro

AU - Kukull, Benjamin

AU - Jensen, Bradden

AU - Corless, Christopher

AU - Margolin, Adam

AU - Gray, Joe W.

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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.

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KW - Quantitative Image Analysis

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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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