Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images
Context: Several therapeutically important mutations in cancers are economically detected using immunohistochemistry (IHC), which highlights the overexpression of specific antigens associated with the mutation. However, IHC panels can be imprecise and relatively expensive in low-income settings. On...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Wolters Kluwer Medknow Publications
2020-01-01
|
Series: | Journal of Pathology Informatics |
Subjects: | |
Online Access: | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=19;epage=19;aulast=Anand |
id |
doaj-6cbaf26728b2403180378eda882c08c2 |
---|---|
record_format |
Article |
spelling |
doaj-6cbaf26728b2403180378eda882c08c22020-11-25T03:45:09ZengWolters Kluwer Medknow PublicationsJournal of Pathology Informatics2153-35392153-35392020-01-01111191910.4103/jpi.jpi_10_20Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue imagesDeepak AnandNikhil Cherian KurianShubham DhageNeeraj KumarSwapnil RanePeter H GannAmit SethiContext: Several therapeutically important mutations in cancers are economically detected using immunohistochemistry (IHC), which highlights the overexpression of specific antigens associated with the mutation. However, IHC panels can be imprecise and relatively expensive in low-income settings. On the other hand, although hematoxylin and eosin (H&E) staining used to visualize the general tissue morphology is a routine and low cost, it does not highlight any specific antigen or mutation. Aims: Using the human epidermal growth factor receptor 2 (HER2) mutation in breast cancer as an example, we strengthen the case for cost-effective detection and screening of overexpression of HER2 protein in H&E-stained tissue. Settings and Design: We use computational methods that reliably detect subtle morphological changes associated with the over-expression of mutation-specific proteins directly from H&E images. Subjects and Methods: We trained a classification pipeline to determine HER2 overexpression status of H&E stained whole slide images. Our training dataset was derived from a single hospital containing 26 (11 HER2+ and 15 HER2–) cases. We tested the classification pipeline on 26 (8 HER2+ and 18 HER2–) held-out cases from the same hospital and 45 independent cases (23 HER2+ and 22 HER2–) from the TCGA-BRCA cohort. The pipeline was composed of a stain separation module and three deep neural network modules in tandem for robustness and interpretability. Statistical Analysis Used: We evaluate our trained model through area under the curve (AUC)-receiver operating characteristic. Results: Our pipeline achieved an AUC of 0.82 (confidence interval [CI]: 0. 65–0. 98) on held-out cases and an AUC of 0.76 (CI: 0. 61–0. 89) on the independent dataset from TCGA. We also demonstrate the region-level correspondence of HER2 overexpression between a patient's IHC and H&E serial sections. Conclusions: Our work strengthens the case for automatically quantifying the overexpression of mutation-specific proteins in H&E-stained digital pathology, and it highlights the importance of multi-stage machine learning pipelines for added robustness and interpretability.http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=19;epage=19;aulast=Anandbreast cancerconvolutional neural networkshistopathologyhuman epidermal growth factor receptor 2immunohistochemistrymutation detectionnucleus detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Deepak Anand Nikhil Cherian Kurian Shubham Dhage Neeraj Kumar Swapnil Rane Peter H Gann Amit Sethi |
spellingShingle |
Deepak Anand Nikhil Cherian Kurian Shubham Dhage Neeraj Kumar Swapnil Rane Peter H Gann Amit Sethi Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images Journal of Pathology Informatics breast cancer convolutional neural networks histopathology human epidermal growth factor receptor 2 immunohistochemistry mutation detection nucleus detection |
author_facet |
Deepak Anand Nikhil Cherian Kurian Shubham Dhage Neeraj Kumar Swapnil Rane Peter H Gann Amit Sethi |
author_sort |
Deepak Anand |
title |
Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images |
title_short |
Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images |
title_full |
Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images |
title_fullStr |
Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images |
title_full_unstemmed |
Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images |
title_sort |
deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images |
publisher |
Wolters Kluwer Medknow Publications |
series |
Journal of Pathology Informatics |
issn |
2153-3539 2153-3539 |
publishDate |
2020-01-01 |
description |
Context: Several therapeutically important mutations in cancers are economically detected using immunohistochemistry (IHC), which highlights the overexpression of specific antigens associated with the mutation. However, IHC panels can be imprecise and relatively expensive in low-income settings. On the other hand, although hematoxylin and eosin (H&E) staining used to visualize the general tissue morphology is a routine and low cost, it does not highlight any specific antigen or mutation. Aims: Using the human epidermal growth factor receptor 2 (HER2) mutation in breast cancer as an example, we strengthen the case for cost-effective detection and screening of overexpression of HER2 protein in H&E-stained tissue. Settings and Design: We use computational methods that reliably detect subtle morphological changes associated with the over-expression of mutation-specific proteins directly from H&E images. Subjects and Methods: We trained a classification pipeline to determine HER2 overexpression status of H&E stained whole slide images. Our training dataset was derived from a single hospital containing 26 (11 HER2+ and 15 HER2–) cases. We tested the classification pipeline on 26 (8 HER2+ and 18 HER2–) held-out cases from the same hospital and 45 independent cases (23 HER2+ and 22 HER2–) from the TCGA-BRCA cohort. The pipeline was composed of a stain separation module and three deep neural network modules in tandem for robustness and interpretability. Statistical Analysis Used: We evaluate our trained model through area under the curve (AUC)-receiver operating characteristic. Results: Our pipeline achieved an AUC of 0.82 (confidence interval [CI]: 0. 65–0. 98) on held-out cases and an AUC of 0.76 (CI: 0. 61–0. 89) on the independent dataset from TCGA. We also demonstrate the region-level correspondence of HER2 overexpression between a patient's IHC and H&E serial sections. Conclusions: Our work strengthens the case for automatically quantifying the overexpression of mutation-specific proteins in H&E-stained digital pathology, and it highlights the importance of multi-stage machine learning pipelines for added robustness and interpretability. |
topic |
breast cancer convolutional neural networks histopathology human epidermal growth factor receptor 2 immunohistochemistry mutation detection nucleus detection |
url |
http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=19;epage=19;aulast=Anand |
work_keys_str_mv |
AT deepakanand deeplearningtoestimatehumanepidermalgrowthfactorreceptor2statusfromhematoxylinandeosinstainedbreasttissueimages AT nikhilcheriankurian deeplearningtoestimatehumanepidermalgrowthfactorreceptor2statusfromhematoxylinandeosinstainedbreasttissueimages AT shubhamdhage deeplearningtoestimatehumanepidermalgrowthfactorreceptor2statusfromhematoxylinandeosinstainedbreasttissueimages AT neerajkumar deeplearningtoestimatehumanepidermalgrowthfactorreceptor2statusfromhematoxylinandeosinstainedbreasttissueimages AT swapnilrane deeplearningtoestimatehumanepidermalgrowthfactorreceptor2statusfromhematoxylinandeosinstainedbreasttissueimages AT peterhgann deeplearningtoestimatehumanepidermalgrowthfactorreceptor2statusfromhematoxylinandeosinstainedbreasttissueimages AT amitsethi deeplearningtoestimatehumanepidermalgrowthfactorreceptor2statusfromhematoxylinandeosinstainedbreasttissueimages |
_version_ |
1724510883533553664 |