PET radiomicsdimensionality reductionfeature calculation and selectionradiomic featuresradiomics
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Abstract (AI)
Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics-the so-called radiomic features-within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and may, alone or in combination with demographic, histologic, genomic, or proteomic data, be used for clinical problem solving. The goal of this continuing education article is to provide an introduction to the field, covering the basic radiomics workflow: feature calculation and selection, dimensionality reduction, and data processing. Potential clinical applications in nuclear medicine that include PET radiomics-based prediction of treatment response and survival will be discussed. Current limitations of radiomics, such as sensitivity to acquisition parameter variations, and common pitfalls will also be covered.
Key Findings
1
PET radiomics has potential clinical applications in predicting treatment response and survival in nuclear medicine.
2
Radiomic features can be combined with demographic, histologic, genomic, or proteomic data for clinical problem solving.
3
Radiomics extracts quantitative features from medical images that capture tissue and lesion characteristics such as heterogeneity and shape.
4
Radiomics is sensitive to variations in image acquisition parameters, representing a current limitation and common pitfall.
5
The basic radiomics workflow includes feature calculation and selection, dimensionality reduction, and data processing.
Research Object
Radiomic features extracted from medical images
Research Subject
The workflow and properties of radiomics including feature calculation and selection, dimensionality reduction, data processing, clinical applications (e.g., PET-based prediction of treatment response and survival), and limitations such as sensitivity to acquisition parameter variations
Publication Details
Publication Date
2020-02-14
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