Big Data and machine learning in radiation oncology: State of the art and future prospects.
Journal:
Cancer letters
Published Date:
May 27, 2016
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
Keywords
Biomedical Research
Data Interpretation, Statistical
Data Mining
Databases, Factual
Decision Support Techniques
Electronic Health Records
Humans
Machine Learning
Neoplasms
Neural Networks, Computer
Radiation Dosage
Radiation Oncology
Radiotherapy
Radiotherapy Planning, Computer-Assisted
Risk Assessment
Risk Factors
Support Vector Machine
Unsupervised Machine Learning