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Aritficial intellegence in breast MRI radiogenomics: towards accurate prediction of neoadjuvant vhemotherapy responses

Yin, X.-X., Jin, Y., Gao, M. and Hadjiloucas, S. ORCID: https://orcid.org/0000-0003-2380-6114 (2020) Aritficial intellegence in breast MRI radiogenomics: towards accurate prediction of neoadjuvant vhemotherapy responses. Current Medical Imaging. ISSN 1875-6603 (In Press)

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To link to this item DOI: 10.2174/1573405616666200825161921

Abstract/Summary

Neoadjuvant Chemotherapy (NAC) in breast cancer patients has considerable prognostic and treatment potential and can be tailored to individual patients as part of precision medicine protocols. This work reviews recent advances in artificial intelligence so as to enable the use of radiogeomics for the accurate NAC analysis and prediction. The work addresses a new problem in radiogenomics mining: How to combine structural radiomics information and non-structural genomics information for accurate NAC prediction. This requires the automated extraction of parameters from structural breast radiomics data, and finding non-structural feature vectors with diagnostic value, which then are combined with genomics data acquired from exocrine bodies in blood samples from a cohort of cancer patients to enable accurate NAC prediction. A self-attention-based deep learning approach along with an effective multi-channel tumour image reconstruction algorithm of high dimensionality is proposed. The aim is to generate non-structural feature vectors for accurate prediction of the NAC responses by combining imaging datasets with exocrine body related genomics analysis.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:92708
Publisher:Bentham Science

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