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.
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Cancer registries often lack the granularity of SDoH variables, unstructured clinical narratives, and imaging data needed to power next-generation predictive models.
“Cancer registries represent one important source of such real-world data (RWD), yet they often lack the granularity of social determinants of health (SDoH) variables, unstructured clinical narratives, and imaging data needed to power next-generation predictive models.6”
From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [6]
Integration of varied data sources, including imaging, dosimetry, and EMR, has enabled creation of comprehensive predictive models in Radiation Oncology.
“The integration of varied data sources, including imaging, dosimetry, and electronic medical record (EMR), has enabled the creation of comprehensive predictive models in Radiation Oncology.6”
From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [6]
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- Authors
- Bibault, J.-E., Giraud, P., & Burgun, A.
- Venue
- Cancer Letters
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 382
- Issue
- 1
- Pages
- 110-117
- ISSN
- 0304-3835
- Cited by
- 298
- References
- 5
- Language
- en
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