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

Bibault Jean-Emmanuel, Giraud Philippe, Burgun Anita

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. © 2016 Elsevier Ireland Ltd. All rights reserved.

2016. Cancer Lett.; 382(1):110-117

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