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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, Jon; Soria, Daniele; Green, Andrew; Ellis, Ian O.; Zou, Wenbin; Qiu, Guoping

Authors

Jingxin Liu

Bolei Xu

Chi Zheng

Yuanhao Gong

Daniele Soria

Wenbin Zou

GUOPING QIU GUOPING.QIU@NOTTINGHAM.AC.UK
Professor of Visual Information Processing



Abstract

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumor, 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 (tumor and non-tumor), a second FCN to extract tumor 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 an 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 the 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.

Citation

Liu, J., Xu, B., Zheng, C., Gong, Y., Garibaldi, J., Soria, D., …Qiu, G. (2019). An end-to-end deep learning histochemical scoring system for breast cancer TMA. IEEE Transactions on Medical Imaging, 38(2), 617-628. https://doi.org/10.1109/TMI.2018.2868333

Journal Article Type Article
Acceptance Date Sep 12, 2018
Online Publication Date Sep 3, 2018
Publication Date 2019-02
Deposit Date Sep 19, 2018
Publicly Available Date Mar 29, 2024
Journal IEEE Transactions on Medical Imaging
Print ISSN 0278-0062
Electronic ISSN 1558-254X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 38
Issue 2
Pages 617-628
DOI https://doi.org/10.1109/TMI.2018.2868333
Keywords H-Score, immunohistochemistry, diaminobenzidine, convolutional neural network, breast cancer
Public URL https://nottingham-repository.worktribe.com/output/1073445
Publisher URL https://ieeexplore.ieee.org/abstract/document/8453832/
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