Radiomics: a new application from established techniques

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.

Advancements in magnetic resonance imaging‐based biomarkers for muscular dystrophy

“…Radiomics, or the extraction of quantitative data based on the gray‐level intensity of multiple images, is increasingly being used by imaging research teams in various diseases, including the muscular dystrophies . Radiomic techniques include texture analysis, which produces quantitative metrics of radiologic images by using first‐ and second‐order statistics .…”

Classification of malignant and benign liver tumors using a radiomics approach

“…State-of-the-art radiomics features 16 quantifying shape, texture, heterogeneity (Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) 17 ) and intensity were extracted from the ROI. Texture features were constructed using the Gray Level Co-occurence Matrix (GLCM), Gray Level Size Zone Matrix (GLSZM), Gray Level Run Length Matrix (GLRLM), Gabor filters and Local Binary Patterns (LBP).…”

Initial assessment of 3D magnetic resonance fingerprinting (MRF) towards quantitative brain imaging for radiation therapy

“…For example, quantitative analysis of intensity‐normalized longitudinal T1‐/T2‐weighted MRIs could distinguish radiation necrosis from tumor regression of brain metastasis and identify hypoxia pattern, an important radio‐resistant mechanism, in glioblastoma . However, any normalization approach will introduce bias to the normalized MRI data, which potentially leads to unwanted alternations of statistical properties of the MRI data and possible degradation of statistical power …”

“…15 However, any normalization approach will introduce bias to the normalized MRI data, which potentially leads to unwanted alternations of statistical properties of the MRI data and possible degradation of statistical power. [16][17][18] Magnetic resonance fingerprinting (MRF) has unique strength of simultaneous acquisitions of multiple inherently quantitative maps of intrinsic tissue properties, 19 including T1/T2 relaxation times, within a clinically reasonable time frame of 5-10 min. These characteristics makes MRF a strong candidate as the routine MRI protocol for radiation treatment of brain tumors to satisfy increasing clinical and research needs of quantitative analysis.…”