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

Jan C. Peeken, Benedikt Wiestler, Stephanie E. Combs

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

16 Scopus citations

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.

Original languageEnglish
Title of host publicationRecent Results in Cancer Research
PublisherSpringer
Pages773-794
Number of pages22
DOIs
StatePublished - 2020
Externally publishedYes

Publication series

NameRecent Results in Cancer Research
Volume216
ISSN (Print)0080-0015
ISSN (Electronic)2197-6767

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