Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics

TRAN, William, JERZAK, Katarzyna, LU, Fang-I, KLEIN, Jonathan, TABBARAH, Sami, LAGREE, Andrew, WU, Tina, ROSADO-MENDEZ, Ivan, LAW, Ethan, SAEDNIA, Khadijeh and SADEGHI-NAINI, Ali (2019). Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics. Journal of Medical Imaging and Radiation Sciences, 50 (4), S32-S41.

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Official URL: https://www.sciencedirect.com/science/article/pii/...
Link to published version:: https://doi.org/10.1016/j.jmir.2019.07.010
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    Abstract

    Progress in computing power and advances in medical imaging over recent decades have culminated in new opportunities for artificial intelligence (AI), computer vision, and using radiomics to facilitate clinical decision-making. These opportunities are growing in medical specialties, such as radiology, pathology, and oncology. As medical imaging and pathology are becoming increasingly digitized, it is recently recognized that harnessing data from digital images can yield parameters that reflect the underlying biology and physiology of various malignancies. This greater understanding of the behaviour of cancer can potentially improve on therapeutic strategies. In addition, the use of AI is particularly appealing in oncology to facilitate the detection of malignancies, to predict the likelihood of tumor response to treatments, and to prognosticate the patients' risk of cancer-related mortality. AI will be critical for identifying candidate biomarkers from digital imaging and developing robust and reliable predictive models. These models will be used to personalize oncologic treatment strategies, and identify confounding variables that are related to the complex biology of tumors and diversity of patient-related factors (ie, mining “big data”). This commentary describes the growing body of work focussed on AI for precision oncology. Advances in AI-driven computer vision and machine learning are opening new pathways that can potentially impact patient outcomes through response-guided adaptive treatments and targeted therapies based on radiomic and pathomic analysis.

    Item Type: Article
    Additional Information: ** Article version: AM ** Embargo end date: 12-12-2020 ** From Elsevier via Jisc Publications Router ** Licence for AM version of this article starting on 12-12-2020: http://creativecommons.org/licenses/by-nc-nd/4.0/ **Journal IDs: issn 19398654 **History: issue date 31-12-2019; published_online 12-12-2019
    Identification Number: https://doi.org/10.1016/j.jmir.2019.07.010
    Page Range: S32-S41
    SWORD Depositor: Justine Gavin
    Depositing User: Justine Gavin
    Date Deposited: 16 Dec 2019 10:37
    Last Modified: 16 Dec 2019 10:37
    URI: http://shura.shu.ac.uk/id/eprint/25552

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