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An end-to-end deep learning histochemical scoring system for breast cancer TMA

Liu, Jingxin, Xu, Bolei, Zheng, Chi, Gong, Yuanhao, Garibaldi, Jonathan M., Soria, Daniele, Green, Andrew R., Ellis, Ian O., Zou, Wenbin, Qiu, Guoping and others. (2019) An end-to-end deep learning histochemical scoring system for breast cancer TMA. IEEE Transactions on Medical Imaging, 38 (2). pp. 617-628. ISSN 0278-0062. (doi:10.1109/TMI.2018.2868333) (KAR id:77395)

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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.

Item Type: Article
DOI/Identification number: 10.1109/TMI.2018.2868333
Uncontrolled keywords: H-Score, Immunohistochemistry, Diaminobenzidine, Convolutional Neural Network, Breast Cancer
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Daniel Soria
Date Deposited: 13 Oct 2019 16:34 UTC
Last Modified: 16 Feb 2021 14:08 UTC
Resource URI: (The current URI for this page, for reference purposes)
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