Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images

Babak Ehteshami Bejnordi; Jimmy Lin; Ben Glass; Maeve Mullooly; Gretchen L. Gierach; Mark E. Sherman; Nico Karssemeijer; Jeroen Van Der Laak; Andrew H. Beck

doi:10.1109/ISBI.2017.7950668

Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images

Babak Ehteshami Bejnordi, Jimmy Lin, Ben Glass, Maeve Mullooly, Gretchen L. Gierach, Mark E. Sherman, Nico Karssemeijer, Jeroen Van Der Laak, Andrew H. Beck

Quantitative Health Sciences

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

23 Scopus citations

Abstract

Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we propose a system for classification of hematoxylin and eosin (H&E) stained breast specimens based on convolutional neural networks that primarily targets the assessment of tumor-associated stroma to diagnose breast cancer patients. We evaluate the performance of our proposed system using a large cohort containing 646 breast tissue biopsies. Our evaluations show that the proposed system achieves an area under ROC of 0.92, demonstrating the discriminative power of previously neglected tumor associated stroma as a diagnostic biomarker.

Original language	English (US)
Title of host publication	2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
Publisher	IEEE Computer Society
Pages	929-932
Number of pages	4
ISBN (Electronic)	9781509011711
DOIs	https://doi.org/10.1109/ISBI.2017.7950668
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

Other

Other	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Country/Territory	Australia
City	Melbourne
Period	4/18/17 → 4/21/17

Keywords

Breast Cancer
Convolutional Neural Networks
Digital pathology
Tumor Associated Stroma

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2017.7950668

Cite this

Ehteshami Bejnordi, B., Lin, J., Glass, B., Mullooly, M., Gierach, G. L., Sherman, M. E., Karssemeijer, N., Van Der Laak, J., & Beck, A. H. (2017). Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 (pp. 929-932). Article 7950668 (Proceedings - International Symposium on Biomedical Imaging). IEEE Computer Society. https://doi.org/10.1109/ISBI.2017.7950668

Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images. / Ehteshami Bejnordi, Babak; Lin, Jimmy; Glass, Ben et al.
2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society, 2017. p. 929-932 7950668 (Proceedings - International Symposium on Biomedical Imaging).

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

Ehteshami Bejnordi, B, Lin, J, Glass, B, Mullooly, M, Gierach, GL, Sherman, ME, Karssemeijer, N, Van Der Laak, J & Beck, AH 2017, Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images. in 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017., 7950668, Proceedings - International Symposium on Biomedical Imaging, IEEE Computer Society, pp. 929-932, 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, Australia, 4/18/17. https://doi.org/10.1109/ISBI.2017.7950668

Ehteshami Bejnordi B, Lin J, Glass B, Mullooly M, Gierach GL, Sherman ME et al. Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society. 2017. p. 929-932. 7950668. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2017.7950668

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abstract = "Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we propose a system for classification of hematoxylin and eosin (H&E) stained breast specimens based on convolutional neural networks that primarily targets the assessment of tumor-associated stroma to diagnose breast cancer patients. We evaluate the performance of our proposed system using a large cohort containing 646 breast tissue biopsies. Our evaluations show that the proposed system achieves an area under ROC of 0.92, demonstrating the discriminative power of previously neglected tumor associated stroma as a diagnostic biomarker.",

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doi = "10.1109/ISBI.2017.7950668",

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AU - Gierach, Gretchen L.

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AU - Karssemeijer, Nico

AU - Van Der Laak, Jeroen

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N2 - Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we propose a system for classification of hematoxylin and eosin (H&E) stained breast specimens based on convolutional neural networks that primarily targets the assessment of tumor-associated stroma to diagnose breast cancer patients. We evaluate the performance of our proposed system using a large cohort containing 646 breast tissue biopsies. Our evaluations show that the proposed system achieves an area under ROC of 0.92, demonstrating the discriminative power of previously neglected tumor associated stroma as a diagnostic biomarker.

AB - Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we propose a system for classification of hematoxylin and eosin (H&E) stained breast specimens based on convolutional neural networks that primarily targets the assessment of tumor-associated stroma to diagnose breast cancer patients. We evaluate the performance of our proposed system using a large cohort containing 646 breast tissue biopsies. Our evaluations show that the proposed system achieves an area under ROC of 0.92, demonstrating the discriminative power of previously neglected tumor associated stroma as a diagnostic biomarker.

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Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images

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