Radiomics (as applied to radiology) is a field of medical study that aims to extract a large number of quantitative features from medical images using data characterization algorithms. The data is assessed for improved decision support. It has the potential to uncover disease characteristics that are difficult to identify by human vision alone.
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Process
The process of creating a database of correlative quantitative features, which can be used to analyze subsequent (unknown) cases includes the following steps 3.
Initial image processing
Using a variety of reconstruction algorithms such as contrast, edge enhancement, etc. influences the quality and usability of the images, which in turn determines how easily and accurately an abnormal characteristic could be detected and characterized.
Image segmentation
Identify/create areas (2D images) or volumes of interest (3D images). Can be done either manually, semi-automated, or fully automated using artificial intelligence.
For large data sets, an automated process is needed because manual techniques are usually very time-consuming and tend to be less accurate, less reproducible and less consistent compared with automated artificial intelligence techniques.
NB: many tools allow manual checking and adjustment of automated outputs, which is recommended especially with tools that use atlas based segmentation.
Features extraction and qualification
Features include volume, shape, surface, density, and intensity, texture, location, and relations with the surrounding tissues.
Semantic features are those that are commonly used in the radiology lexicon to describe regions of interest.
Agnostic features are those that attempt to capture lesion heterogeneity through quantitative mathematical descriptors.
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
Uses
Radiomics can be applied to most imaging modalities including radiographs, ultrasound, CT, MRI and PET studies. It can be used to increase the precision in the diagnosis, assessment of prognosis, and prediction of therapy response, particularly in combination with clinical, biochemical, and genetic data. The technique has been used in oncological studies, but potentially can be applied to any disease.
A typical example of radiomics is using texture analysis to correlate molecular and histological features of diffuse high-grade gliomas 2.
The determination of most discriminatory radiomics feature extraction methods varies with the modality of imaging and the pathology studied.
Challenges include the full adoption of a common nomenclature, image data sharing, large computing power and storage requirements, and validating models across different imaging platforms and patient populations. Some of the challenges are being addressed by the increasing adoption of standards, such as the IBSI standards 4, and scoring such as the radiomics quality score (RQS) 5.