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Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images

Ortega-Ruiz, M. A., Karabağ, C. ORCID: 0000-0003-4424-0471, Garduño, V. G. & Reyes-Aldasoro, C. C. ORCID: 0000-0002-9466-2018 (2020). Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images. Journal of Imaging, 6(10), article number 101. doi: 10.3390/jimaging6100101


This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu’s binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches.

Publication Type: Article
Additional Information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords: neo-adjuvant treatment; digital pathology; tumour cellularity; machine learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Science & Technology > Computer Science
School of Science & Technology > Computer Science > giCentre
SWORD Depositor:
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Available under License Creative Commons: Attribution International Public License 4.0.

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