Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy Journal Article

   

Authors:	Lo Gullo, R.; Eskreis-Winkler, S.; Morris, E. A.; Pinker, K.
Article Title:	Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy
Abstract:	In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR), some achieve a partial response, and some do not respond at all or even progress. Accurate prediction of treatment response has the potential to improve patient care by improving prognostication, enabling de-escalation of toxic treatment that has little benefit, facilitating upfront use of novel targeted therapies, and avoiding delays to surgery. Visual inspection of a patient's tumor on multiparametric MRI is insufficient to predict that patient's response to NAC. However, machine learning and deep learning approaches using a mix of qualitative and quantitative MRI features have recently been applied to predict treatment response early in the course of or even before the start of NAC. This is a novel field but the data published so far has shown promising results. We provide an overview of the machine learning and deep learning models developed to date, as well as discuss some of the challenges to clinical implementation. (C) 2019 The Author(s). Published by Elsevier Ltd.
Keywords:	accuracy; mammography; artificial intelligence; neoadjuvant chemotherapy; tumor; spectroscopy; mri; pathological response; machine learning; cancer; multiparametric mri; 1st cycle
Journal Title:	Breast
Volume:	49
ISSN:	0960-9776
Publisher:	Elsevier Inc.
Date Published:	2020-02-01
Start Page:	115
End Page:	122
Language:	English
ACCESSION:	WOS:000512925000017
DOI:	10.1016/j.breast.2019.11.009
PROVIDER:	wos
PUBMED:	31786416
PMCID:	PMC7375548
Notes:	Article -- Source: Wos

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