Big Data and machine learning in radiation oncology: State of the art and future prospects

被引：196

作者：

Bibault, Jean-Emmanuel ^{[1
,2
]}

Giraud, Philippe ^{[1
]}

Burgun, Anita ^{[2
,3
]}

机构：

[1] Paris Descartes Univ, Georges Pompidou European Hosp, AP HP, Sorbonne Paris Cite,Radiat Oncol Dept, Paris, France

[2] Paris Descartes Univ, Sorbonne Paris Cite, INSERM, Team Informat Sci Support Personalized Med 22,UMR, Paris, France

[3] Paris Descartes Univ, Georges Pompidou European Hosp, AP HP, Sorbonne Paris Cite,Biomed Informat & Publ Hlth D, Paris, France

来源：

CANCER LETTERS | 2016年 / 382卷 / 01期

关键词：

Radiation oncology; Big Data; Predictive model; Machine learning; ARTIFICIAL NEURAL-NETWORK; DOSE-VOLUME; BREAST-CANCER; LUNG-CANCER; NECK-CANCER; THERAPY; RADIOTHERAPY; PREDICTION; OUTCOMES; HEAD;

D O I：

10.1016/j.canlet.2016.05.033

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Precision medicine relies on an increasing amount of heterogeneous data. Advances in radiation oncology, through the use of CT Scan, dosimetry and imaging performed before each fraction, have generated a considerable flow of data that needs to be integrated. In the same time, Electronic Health Records now provide phenotypic profiles of large cohorts of patients that could be correlated to this information. In this review, we describe methods that could be used to create integrative predictive models in radiation oncology. Potential uses of machine learning methods such as support vector machine, artificial neural networks, and deep learning are also discussed. (C) 2016 Elsevier Ireland Ltd. All rights reserved.

引用

页码：110 / 117

页数：8

共 63 条

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