Abstract
Radiomics is a promising method to quantify and describe the tumor phenotype on medical images. High numbers of image features are extracted from medical images and can be used within a clinical decision support system by integrating this data with clinical and pathological variables. Herein, we give a short introduction into this image analysis method and present an overview on the workflow.
Highlights
Varying outcomes of anticancer treatment for the same tumor histology and site have led to new insights in cancer biology and oncological research
In contrast to radiological reports, which nowadays are still descriptive and written in unstructured text form without many measurements, radiomics is able to perform a quantitative image analysis aiming at a comprehensive and reproducible characterization of medical images. By integrating these data with clinical variables, radiomics models have the potential to be used in a clinical decision support system (CDSS) to guide clinical decision-making
Even though this review focusses on its application in oncology, radiomics is used in non-malignant disorders [4]
Summary
More precisely the working steps of preprocessing of images and feature extraction, is done using specific software. To develop such software on its own, coding skills are required. It is useful if the software can handle all types of imaging data such as CT, MRI or PET and is not limited in its scope. Imaging (Fig. 1a): A great advantage of radiomics is that medical images, the mainstay of a radiomics analysis, are regular part of the clinical routine.
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