Universität zu Köln

Lohmann, Philipp ORCID: 0000-0002-5360-046X, Bousabarah, Khaled, Hoevels, Mauritius and Treuer, Harald (2020). Radiomics in radiation oncology-basics, methods, and limitations. Strahlenther. Onkol., 196 (10). S. 848 - 856. HEIDELBERG: SPRINGER HEIDELBERG. ISSN 1439-099X

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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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Lohmann, Philipp UNSPECIFIED orcid.org/0000-0002-5360-046X UNSPECIFIED Bousabarah, Khaled UNSPECIFIED UNSPECIFIED UNSPECIFIED Hoevels, Mauritius UNSPECIFIED UNSPECIFIED UNSPECIFIED Treuer, Harald UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-326682
DOI:	10.1007/s00066-020-01663-3
Journal or Publication Title:	Strahlenther. Onkol.
Volume:	196
Number:	10
Page Range:	S. 848 - 856
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 NORMALIZATION TECHNIQUES; BRAIN METASTASES; SEGMENTATION; IMAGES; FEATURES Multiple languages Oncology; Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/32668