Radiomics

Last updated Jun 24, 2022

• Type atom
• Status good
• On radiomics
• On medical imaging

1

Radiomics is a field of medical study that aims to extract a large number of quantitative features from medical images using data characterization algorithms.

Source: Radiomics | Radiology Reference Article | Radiopaedia.org

The process of extracting quantitative features from medical images (.dicom) can be briefly described as:

• First, some preprocessing steps are performed to increase the quality and usability of the image, e.g. contrast, edge enhancement etc.
• A region of interest (ROI) - area of interest (2D images) or volume of interest (3D images) - is identified either manually, semi-automated, or fully automated using artificial intelligence.
• High dimension features are extracted from these ROIs that include semantic and agnostic features.
  • Semantic features are morphological features that are commonly used in radiology reports to describe lesions.
  • Agnostic features are more complex mathematically extracted quantitative features.
  • Examples of semantic features
    • shape
    • location
    • vascularity
    • spiculation
    • necrosis
    • attachments
  • Equivalent examples of agnostic features
    • kurtosis or skewness (of the image histogram)
    • Haralick textures
    • Laws textures
    • wavelets
    • Laplacian transforms
    • Minkowski functions
    • fractal dimensions
• These extracted features are then used to generate a report, which is placed in a database along with other data, such as clinical and genomic data such as genes, mutations, and expression patterns.

Sources:

• Combining molecular and imaging metrics in cancer: radiogenomics | Insights into Imaging | Full Text (springeropen.com)
• Radiomics | Radiology Reference Article | Radiopaedia.org

Related: Magnetic resonance imaging (MRI)