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Expert Rev Precis Med Drug Dev. 2016;1(2):207-226. doi: 10.1080/23808993.2016.1164013. Epub 2016 Mar 31.

Radiomics: a new application from established techniques.

Author information

1
The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD 21205; Department of Computer Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205.
2
The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD 21205; Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205.

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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