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Open Access Green as soon as Postprint is submitted to ZB.
Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application.
In: Molecular Imaging in Oncology. Springer, 2020. 773-794 (Recent Results Cancer Res. ; 216)
Medical imaging plays an imminent role in today's radiation oncology workflow. Predominantly based on semantic image analysis, malignant tumors are diagnosed, staged, and therapy decisions are made. The field of "radiomics" promises to extract complementary, objective information from medical images. In radiomics, predefined quantitative features including intensity statistics, texture, shape, or filtering techniques are combined into statistical or machine learning models to predict clinical or biological outcomes. Alternatively, deep neural networks can directly analyze medical images and provide predictions. A large number of research studies could demonstrate that radiomics prediction models may provide significant benefits in the radiation oncology workflow including diagnostics, tumor characterization, target volume segmentation, prognostic stratification, and prediction of therapy response or treatment-related toxicities. This chapter provides an overview of techniques within the radiomics toolbox, potential clinical application, and current limitations. A literature overview of four selected malignant entities including non-small cell lung cancer, head and neck squamous cell carcinomas, soft tissue sarcomas, and gliomas is given.
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Publication type
Article: Edited volume or book chapter
ISSN (print) / ISBN
0080-0015
Book Volume Title
Molecular Imaging in Oncology
Quellenangaben
Volume: 216,
Pages: 773-794
Publisher
Springer
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Radiation Medicine (IRM)