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| Hauptseite > Publikationsdatenbank > Radiomics in radiation oncology—basics, methods, and limitations |
| Hauptseite > Publikationsdatenbank > Radiomics in radiation oncology—basics, methods, and limitations |
| Journal Article | FZJ-2020-02395 |
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2020
Springer Medizin
Heidelberg
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Please use a persistent id in citations: http://hdl.handle.net/2128/25769 doi:10.1007/s00066-020-01663-3
Abstract: Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.
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