Journal of Nuclear Medicine· 2020Q1
Introduction to Radiomics
- 1,738citations
- Q1SCImago
- 2020year
Short summary
Radiomics extracts quantitative features from medical images to characterize tissue heterogeneity and shape, enabling improved clinical decision-making when combined with other patient data.
AI-generated from the title and abstract; the full text is not read.
Key points
- Radiomics extracts quantitative features from medical images to describe tissue characteristics.
- These features capture heterogeneity and shape, aiding in clinical problem-solving.
- The radiomics workflow involves feature calculation, selection, dimensionality reduction, and data processing.
- Potential applications include predicting treatment response and survival in nuclear medicine (e.g., PET radiomics).
- Limitations include sensitivity to acquisition parameter variations and common pitfalls.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Journal of Nuclear Medicine, 2020 · DOI ↗
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Field: Radiology, Nuclear Medicine and Imaging
Radiology, Nuclear Medicine and ImagingMedicine