Journal of Pathology Informatics Journal of Pathology Informatics
Contact us | Home | Login   |  Users Online: 38379  Print this pageEmail this pageSmall font sizeDefault font sizeIncrease font size 


RESEARCH ARTICLE
Year : 2020  |  Volume : 11  |  Issue : 1  |  Page : 19

Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images


1 Department of Electrical Engineering, IIT Bombay, Mumbai, Maharashtra, India
2 Department of Computing Science, University of Alberta, Edmonton; Alberta Machine Intelligence Institute, Edmonton, Canada
3 Department of Pathology, Tata Memorial Centre - ACTREC, HBNI, Navi Mumbai, Maharashtra, India
4 Department of Pathology, University of Illinois, Chicago, USA
5 Department of Electrical Engineering, IIT Bombay, Mumbai, Maharashtra, India; Department of Pathology, University of Illinois, Chicago, USA

Correspondence Address:
Mr. Deepak Anand
Department of Electrical Engineering, IIT Bombay, Powai, Mumbai - 400 076, Maharashtra
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_10_20

Rights and Permissions

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.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed445    
    Printed29    
    Emailed0    
    PDF Downloaded108    
    Comments [Add]    

Recommend this journal