This title appears in the Scientific Report :
2020
Please use the identifier:
http://dx.doi.org/10.1007/s00066-020-01626-8 in citations.
Please use the identifier: http://hdl.handle.net/2128/25766 in citations.
Applications of radiomics and machine learning for radiotherapy of malignant brain tumors
Applications of radiomics and machine learning for radiotherapy of malignant brain tumors
BackgroundMagnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumo...
| Personal Name(s): | Kocher, Martin (Corresponding author) |
|---|---|
| Ruge, Maximilian I. / Galldiks, Norbert / Lohmann, Philipp | |
| Contributing Institute: |
Physik der Medizinischen Bildgebung; INM-4 Kognitive Neurowissenschaften; INM-3 |
| Published in: | Strahlentherapie und Onkologie, 196 (2020) S. 856–867 |
| Imprint: |
Heidelberg
Springer Medizin
2020
|
| DOI: |
10.1007/s00066-020-01626-8 |
| PubMed ID: |
32394100 |
| Document Type: |
Journal Article |
| Research Program: |
(Dys-)function and Plasticity |
| Link: |
OpenAccess OpenAccess |
| Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/25766 in citations.
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| 520 | |a BackgroundMagnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors.MethodsThis study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases.ResultsFeature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods.ConclusionClinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied. | ||
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