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. 2016;1(2):207-226.
doi: 10.1080/23808993.2016.1164013. Epub 2016 Mar 31.

Radiomics: a new application from established techniques

Vishwa Parekh  1 , Michael A Jacobs  2
Affiliations

Radiomics: a new application from established techniques

Vishwa Parekh et al. Expert Rev Precis Med Drug Dev. 2016.

Abstract

The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of "big data". Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.

Keywords: ADC map; Breast; DWI; Genetics; Magnetic Resonance Imaging; Radiomics; cancer; diffusion-weighted imaging; informatics; machine learning; proton; texture; treatment response.

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Figures

Figure 1
Figure 1
Illustration of the radiomics algorithm. (a) Initial Computed Tomography (CT) scan. (b) Segmentation is performed on the lesion using a region of interest(ROI). (c) Radiomic features are extracted from the ROI based on the gray level patterns, inter-voxel relationships, and shape. (d) A subset of the extracted radiomic features is selected for classification (e) The selected features are used as inputs into a classification model to produce a diagnosis or correlation to a prognostic marker. Data from,.
Figure 2
Figure 2
Illustration of statistical texture feature extraction. (a) Segmented tumor image (b) Segmented tumor image quantized to four intensity levels (c) First order statistical features corresponding to first order histogram (d) Higher order statistical features corresponding to 1. GLCM (gray level co-occurrence matrix), 2. GLRLM (gray level run length matrix) and 3. NGTDM (neighborhood gray tone difference matrix). Data from.
Figure 3
Figure 3
(a) Illustration of the inter-pixel relationships characterized by the user defined parameter, θ (b) An example 5 × 5 matrix with gray values ranging from 1 to 5. (c) The resultant symmetric gray level co-occurrence matrix (GLCM) obtained by multiplying the asymmetric GLCM with its transpose.
Figure 4
Figure 4
(a) Illustration of the inter-pixel relationships characterized by the user defined parameters, angle θ and run length j. (b) Example 5 × 5 matrix with values ranging from 1 to 5. (c) Resultant gray level run length matrix (GLRL) for run lengths of 1 to 5 and θ = 0°.
Figure 5
Figure 5
(a) Illustration of the neighborhood around the pixel of interest based on the user defined neighborhood parameter, d (b) Example 5×5 input matrix with values ranging from 1 to 5. (c) Neighborhood gray tone difference matrix for d=1.
Figure 6
Figure 6
Illustration of different techniques used for spatial domain filtering (a) statistical kernel (e.g. median filter) (b) Edge kernel (e.g. Laplacian of Gaussian filter) (c) Special kernel (e.g. Fractal dimension filter). Data from,,
Figure 7
Figure 7
Multiresolution methods applied to a diffusion-weighted image (b=500): a) the original size 256×256; b) compressed image (64×64) at different levels. c) For compression 2D biorthogonal spline wavelets were used. d(h)j, d(v)j and d(d)j respectively are detail components corresponding to vertical, horizontal, and diagonal. aj, is the approximation (coarse) component at decomposition level. Data from
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