Applications of radiomics and machine learning for radiotherapy of malignant brain tumors
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REVIEW ARTICLE
Applications of radiomics and machine learning for radiotherapy of malignant brain tumors Martin Kocher1,2,4
· Maximilian I. Ruge2,4 · Norbert Galldiks1,3,4 · Philipp Lohmann1,2
Received: 29 March 2020 / Accepted: 22 April 2020 © The Author(s) 2020
Abstract Background Magnetic 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. Methods This 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. Results Feature-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. Conclusion Clinical 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.
Keywords Artificial intelligence · Deep learning · Glioma · Brain metastases · Multiparametric PET/MRI
Introduction Neuroimaging is a field of medical imaging that has attracted the most advanced techniques and methods because Prof. Martin Kocher
[email protected] 1
Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Wilhelm-Johnen-Straße, 52428 Juelich, Germany
2
Department of Stereotaxy and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, Kerpener Str. 62, 50937 Cologne, Germany
3
Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937 Cologne, Germany
4
Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Düsseldorf, Kerpener Str. 62, 50937 Cologne, Germany
of the brain’s complex structure and rich endowment with mole
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