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

被引:54
作者
Lo Gullo, Roberto [1 ]
Eskreis-Winkler, Sarah [1 ]
Morris, Elizabeth A. [1 ]
Pinker, Katja [1 ]
机构
[1] Mem Sloan Kettering Canc Ctr, Breast Imaging Serv, Dept Radiol, 300 W 66th St, New York, NY 10065 USA
基金
美国国家卫生研究院;
关键词
Artificial intelligence; Machine learning; Multiparametric MRI; Neoadjuvant chemotherapy; PATHOLOGICAL RESPONSE; 1ST CYCLE; CANCER; MRI; SPECTROSCOPY; MAMMOGRAPHY; ACCURACY; TUMOR;
D O I
10.1016/j.breast.2019.11.009
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
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.
引用
收藏
页码:115 / 122
页数:8
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