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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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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. This influences the quality and usability of the images, which in turn determines how easily and accurately an abnormal characteristic could be detected and characterized.
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.
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
Equivalent examples of agnostic features
- kurtosis or skewness (of the image histogram)
- Haralick textures
- Laws textures
- Laplacian transforms
- Minkowski functions
- fractal dimensions
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.
Current 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 the radiomics quality score (RQS) 4.
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- 2. AlRayahi J, Zapotocky M, Ramaswamy V, Hanagandi P, Branson H, Mubarak W, Raybaud C, Laughlin S. Pediatric Brain Tumor Genetics: What Radiologists Need to Know. (2018) Radiographics : a review publication of the Radiological Society of North America, Inc. 38 (7): 2102-2122. doi:10.1148/rg.2018180109 - Pubmed
- 3. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, et al Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Pubmed
- 4. Philippe Lambin, Ralph T.H. Leijenaar, Timo M. Deist, Jurgen Peerlings, Evelyn E.C. de Jong, Janita van Timmeren, Sebastian Sanduleanu, Ruben T.H.M. Larue, Aniek J.G. Even, Arthur Jochems, Yvonka van Wijk, Henry Woodruff, Johan van Soest, Tim Lustberg, Erik Roelofs, Wouter van Elmpt, Andre Dekker, Felix M. Mottaghy, Joachim E. Wildberger, Sean Walsh. Radiomics: the bridge between medical imaging and personalized medicine. (2017) Nature Reviews Clinical Oncology. 14 (12): 749. doi:10.1038/nrclinonc.2017.141