Radiomics in neuro-oncology: Basics, workflow, and applications

Philipp Lohmann*, Norbert Galldiks, Martin Kocher, Alexander Heinzel, Christian P. Filss, Carina Stegmayr, Felix M. Mottaghy, Gereon R. Fink, N. Jon Shah, Karl-Josef Langen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

37 Citations (Web of Science)

Abstract

Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and cost-effective evaluation of imaging data is hardly feasible without the support of methods from the field of artificial intelligence (AI). AI can facilitate and shorten various timeconsuming steps in the image processing workflow, e.g., tumor segmentation, thereby optimizing productivity. Besides, the automated and computer-based analysis of imaging data may help to increase data comparability as it is independent of the experience level of the evaluating clinician. Importantly, AI offers the potential to extract new features from the routinely acquired neuroimages of brain tumor patients. In combination with patient data such as survival, molecular markers, or genomics, mathematical models can be generated that allow, for example, the prediction of treatment response or prognosis, as well as the noninvasive assessment of molecular markers. The subdiscipline of AI dealing with the computation, identification, and extraction of image features, as well as the generation of prognostic or predictive mathematical models, is termed radiomics. This review article summarizes the basics, the current workflow, and methods used in radiomics with a focus on feature-based radiomics in neuro-oncology and provides selected examples of its clinical application.

Original languageEnglish
Pages (from-to)112-121
Number of pages10
JournalMethods
Volume188
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Artificial Intelligence
  • Machine learning
  • Deep learning
  • Glioma
  • Brain metastases
  • Multiparametric PET
  • MRI

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