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07:18
Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling
Published on: May 21, 2020
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Quantitative radiomic profiling of glioblastoma represents transcriptomic expression.
Doo-Sik Kong1,2,3, Junhyung Kim2, Gyuha Ryu3
1Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea.
Oncotarget
|February 22, 2018
Summary
Quantitative imaging features can predict glioblastoma molecular subtypes and outcomes. These radiomic profiles correlate with transcriptomic phenotypes, offering noninvasive predictive capabilities for treatment response.
Area of Science:
- Radiomics and Medical Imaging
- Molecular Oncology
- Computational Biology
Background:
- Quantitative imaging biomarkers are emerging tools in medical research.
- Radiomics aims to noninvasively predict tumor genetic changes and treatment outcomes.
Purpose of the Study:
- To identify radiomic features that serve as surrogates for genetic alterations in glioblastoma.
- To establish noninvasive methods for predicting glioblastoma treatment outcomes using imaging data.
Main Methods:
- Retrospective analysis of 65 treatment-naïve glioblastoma patients' preoperative MR imaging data.
- Extraction of 82 radiomic features (first-order statistics, size, volume) from structural and physiological images.
- Unsupervised clustering of radiophenotypic parameters to identify distinct tumor subtypes.
Main Results:
- Three distinct clusters were identified, corresponding to glioblastoma molecular classifications (classical, proneural/neural, mesenchymal).
- These radiomic clusters correlated with differential clinical outcomes.
- Radiomic features reflected transcriptomic phenotypes but not copy number variation or somatic mutations.
Conclusions:
- Quantitative radiomic features show potential for predicting molecular phenotypes and treatment outcomes in glioblastoma.
- Radiomic profiles serve as valuable noninvasive indicators of transcriptomic phenotypes.
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