An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA

Liu, J., Xu, B., Zheng, C., Gong, Y., Garibaldi, J.M., Soria, D., Green, A., Ellis, I.O., Zou, W. and Qiu, G. 2019. An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA. IEEE Transactions on Medical Imaging. 38 (2), pp. 617-628. https://doi.org/10.1109/TMI.2018.2868333

TitleAn End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA
TypeJournal article
AuthorsLiu, J.
Xu, B.
Zheng, C.
Gong, Y.
Garibaldi, J.M.
Soria, D.
Green, A.
Ellis, I.O.
Zou, W.
Qiu, G.
Abstract

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists’ decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumour and non-tumour), a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a
TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists’ scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.

KeywordsH-Score, Immunohistochemistry, Diaminobenzidine, Convolutional Neural Network, Breast Cancer
JournalIEEE Transactions on Medical Imaging
Journal citation38 (2), pp. 617-628
ISSN0278-0062
Year2019
PublisherIEEE
Accepted author manuscript
Digital Object Identifier (DOI)https://doi.org/10.1109/TMI.2018.2868333
Publication dates
Published online03 Sep 2018
Published in printFeb 2019
FunderUniversity of Nottingham

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