Universität zu Köln

Kocher, Martin, Ruge, Maximilian I., Galldiks, Norbert ORCID: 0000-0002-2485-1796 and Lohmann, Philipp ORCID: 0000-0002-5360-046X (2020). Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlenther. Onkol., 196 (10). S. 856 - 868. HEIDELBERG: SPRINGER HEIDELBERG. ISSN 1439-099X

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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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Kocher, Martin UNSPECIFIED UNSPECIFIED UNSPECIFIED Ruge, Maximilian I. UNSPECIFIED UNSPECIFIED UNSPECIFIED Galldiks, Norbert UNSPECIFIED orcid.org/0000-0002-2485-1796 UNSPECIFIED Lohmann, Philipp UNSPECIFIED orcid.org/0000-0002-5360-046X UNSPECIFIED
URN:	urn:nbn:de:hbz:38-334073
DOI:	10.1007/s00066-020-01626-8
Journal or Publication Title:	Strahlenther. Onkol.
Volume:	196
Number:	10
Page Range:	S. 856 - 868
Date:	2020
Publisher:	SPRINGER HEIDELBERG
Place of Publication:	HEIDELBERG
ISSN:	1439-099X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language HIGH-GRADE GLIOMAS; CONVOLUTIONAL NEURAL-NETWORK; DOSE CONFORMAL RADIOTHERAPY; MGMT PROMOTER METHYLATION; RADIATION-THERAPY; PATTERN-ANALYSIS; STEREOTACTIC RADIOSURGERY; 1P/19Q CODELETION; PLUS CONCOMITANT; GLIOBLASTOMA Multiple languages Oncology; Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/33407