Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application

Part of the Recent Results in Cancer Research book series (RECENTCANCER, volume 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.

List of Abbreviations


Brain Tumor Segmentation Challenge


Computational environment for radiological research


Convolutional neural network


Computer tomography


Clinical target volume


Diffusion tensor imaging


Epidermal growth factor receptor


European Organisation for Research and Treatment of Cancer






Gray-level co-occurrence matrix


Gray-level dependence matrix


Gray-level run length matrix


Graphical user interface


Imaging biomarker standardization initiative




Gross tumor volumes


Human papillomavirus


Local binary pattern


Machine learning


Medical imaging tool kit


Magnetic resonance imaging


Multiparametric magnetic resonance imaging


Neighboring gray tone difference matrix


Non-small cell lung cancer


Positron emission tomography


Programmed cell death protein 1


Platelet-derived growth factor receptor A


Anti-programmed cell death ligand 1




Receiver operator characteristic


Standardized uptake value


Tyrosine kinase inhibitor


Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis


Volume of interest


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Radiation OncologyKlinikum Rechts der Isar, Technical University of Munich (TUM)MunichGermany
  2. 2.Department of Radiation Sciences (DRS)Institute of Radiation Medicine (IRM), Helmholtz Zentrum MünchenNeuherbergGermany
  3. 3.Deutsches Konsortium Für Translationale Krebsforschung (DKTK), Partner Site MunichMunichGermany

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