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.

KEYWORDS:

• Radiomics
• informatics
• texture
• machine learning
• Breast
• Magnetic Resonance Imaging
• treatment response
• proton
• diffusion-weighted imaging
• DWI
• ADC map
• cancer
• genetics

Financial & competing interests disclosure

The authors were supported by the following grants: NIH - P50CA103175, 5P30CA06973 (IRAT), 1R01CA190299, U01CA140204 The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.