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

Chapter
Part of the Recent Results in Cancer Research book series (RECENTCANCER, volume 216)

Abstract

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

BraTS

Brain Tumor Segmentation Challenge

CERR

Computational environment for radiological research

CNN

Convolutional neural network

CT

Computer tomography

CTV

Clinical target volume

DTI

Diffusion tensor imaging

EGFR

Epidermal growth factor receptor

EORTC

European Organisation for Research and Treatment of Cancer

FET

18F-fluoroethyl-l-tyrosine

FDG

18F-fluorodeoxyglucose

GLCM

Gray-level co-occurrence matrix

GLDM

Gray-level dependence matrix

GLRLM

Gray-level run length matrix

GUI

Graphical user interface

IBSI

Imaging biomarker standardization initiative

IHC

Immunohistochemistry

GTV

Gross tumor volumes

HPV

Human papillomavirus

LBP

Local binary pattern

ML

Machine learning

MITK

Medical imaging tool kit

MRI

Magnetic resonance imaging

mpMRI

Multiparametric magnetic resonance imaging

NGTDM

Neighboring gray tone difference matrix

NSCLC

Non-small cell lung cancer

PET

Positron emission tomography

PD1

Programmed cell death protein 1

PDGFRA

Platelet-derived growth factor receptor A

PD-L1

Anti-programmed cell death ligand 1

RT

Radiotherapy

ROC

Receiver operator characteristic

SUV

Standardized uptake value

TKI

Tyrosine kinase inhibitor

TRIPOD

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

VOI

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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