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Radiomics refers to the extraction of a large array of quantitative features from imaging that can be correlated with the demographic and genomic profile of the patient.
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Radiomic analysis has the potential to serve as a noninvasive technique for accurate characterization of tumor microenvironment.
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Incorporating simple imaging features, such as tumor location, involvement of eloquent cortex, and extent of the tumor, can improve understanding of tumor genomic profile and aid in therapy planning.
Radiomics in Brain Tumors: An Emerging Technique for Characterization of Tumor Environment
Section snippets
Key points
Radiomics: emerging clinical applications
Radiomics refers to the extraction of a large array of quantitative features from imaging that is correlated with the demographic and genomic profile of the patient. These imaging features comprise of descriptors of size, shape, volume, intensity distribution (extracted from the histogram), and texture patterns. Different imaging modalities (eg, MRI, computed tomography, ultrasound) and different sequences (T1-weighted, T2-weighted, fluid attenuation inversion recovery [FLAIR], diffusion
Big data in medicine: improving 7 billion lives
In recent years, big data has rapidly developed into a strong focus that attracts overwhelming attention from academia, industry, and governments worldwide.78, 79, 80 Although not strictly defined, the term “big data” first appeared in the mid-1990s, gradually becoming noted, and being ushered in as a new buzzword everywhere on the Internet, conferences, scientific publications, competitions, and start-up companies.81, 82, 83, 84, 85 Researchers and policymakers are beginning to realize the
Summary
Radiomic analysis has the potential to serve as a noninvasive technique for accurate characterization of tumor microenvironment, thus improving diagnosis and monitoring of treatment response.93 One of the selling points of radiomic analysis is its ability to be integrated in any current study and in the clinic. This article summarizes several findings in MRI of GBM that indicate its ability to become a powerful biomarker that can be used for tumor grading, prognosis, and identification of
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Cited by (0)
This research is partially funded by the John S. Dunn Sr. Distinguished Chair in Diagnostic Imaging Fund, MD Anderson Brain Tumor Center Program, and MD Anderson Cancer Center startup funding.
The authors have nothing to disclose.