How does Radiomics actually work? – Review

Abstract

Personalized precision medicine requires highly accurate diagnostics. While radiological research has focused on scanner and sequence technologies in recent decades, applications of artificial intelligence are increasingly attracting scientific interest as they could substantially expand the possibility of objective quantification and diagnostic or prognostic use of image information.

In this context, the term “radiomics” describes the extraction of quantitative features from imaging data such as those obtained from computed tomography or magnetic resonance imaging examinations. These features are associated with predictive goals such as diagnosis or prognosis using machine learning models. It is believed that the integrative assessment of the feature patterns thus obtained, in combination with clinical, molecular and genetic data, can enable a more accurate characterization of the pathophysiology of diseases and more precise prediction of therapy response and outcome.

This review describes the classical radiomics approach and discusses the existing very large variability of approaches. Finally, it outlines the research directions in which the interdisciplinary field of radiology and computer science is moving, characterized by increasingly close collaborations and the need for new educational concepts. The aim is to provide a basis for responsible and comprehensible handling of the data and analytical methods used.

Key points: 

• Radiomics is playing an increasingly important role in imaging research.

• Radiomics has great potential to meet the requirements of precision medicine.

• Radiomics analysis is still subject to great variability.

• There is a need for quality-assured application of radiomics in medicine.

Citation Format

• Attenberger UI, Langs G, . How does Radiomics actually work? – Review. Fortschr Röntgenstr 2021; 193: 652 – 657

Key words

machine learning - radiomics - artificial intelligence - features

Publication History

Received: 02 March 2020

Accepted: 05 October 2020

Article published online:
02 December 2020

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