title: Big Data and machine learning in radiation oncology: State of the art and future prospects.
abstract: 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.
authors.affiliation: Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France; INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France. Electronic address: jean-emmanuel.bibault@aphp.fr.
authors.forename: Jean-Emmanuel
authors.initials: JE
authors.lastname: Bibault
authors.affiliation: Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.
authors.forename: Philippe
authors.initials: P
authors.lastname: Giraud
authors.affiliation: INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France; Biomedical Informatics and Public Health Department, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.
authors.forename: Anita
authors.initials: A
authors.lastname: Burgun
mesh_terms: D035843:Biomedical Research / Q000379:methods* / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods* / Q000706:statistics & numerical data; D016208:Databases, Factual / Q000706:statistics & numerical data; D003661:Decision Support Techniques; D057286:Electronic Health Records / Q000706:statistics & numerical data; D006801:Humans; D000069550:Machine Learning; D009369:Neoplasms / Q000473:pathology / Q000532:radiotherapy*; D016571:Neural Networks, Computer; D011829:Radiation Dosage; D018787:Radiation Oncology / Q000379:methods* / Q000706:statistics & numerical data; D011878:Radiotherapy / Q000009:adverse effects; D011880:Radiotherapy Planning, Computer-Assisted; D018570:Risk Assessment; D012307:Risk Factors; D060388:Support Vector Machine; D000069558:Unsupervised Machine Learning
keywords: Big Data; Machine learning; Predictive model; Radiation oncology