Abstract
Radiomics is a medical imaging analysis approachbased on computer-vision. Metabolic radiomics in particular analyses the spatial distribution patterns of molecular metabolism on PET images. Measuring intratumoral heterogeneity via image is one of the main targets of radiomics research, and it aims to build a image-based model for better patient management. The workflow of radiomics using texture analysis follows these steps: 1) imaging (image acquisition and reconstruction); 2) preprocessing (segmentation & quantization); 3) quantification (texture matrix design & texture feature extraction); and 4) analysis (statistics and/or machine learning). The parameters or conditions at each of thesesteps are effect on the results. In statistical testing or modeling, problems such as multiple comparisons, dependence on other variables, and high dimensionality of smallsample size data should be considered. Standardization of methodology and harmonization of image qualityare one of the most important challenges with radiomics methodology. Even though there arecurrent issues in radiomics methodology, it is expected that radiomics will be clinically useful in personalized medicine for oncology.
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