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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Radiogenomics Pilot Study: Association Between Radiomics and Single Nucleotide Polymorphism-Based Microarray Copy

Abeer J Alhussaini1,2,3, Abirami Veluchamy4, Adel Jawli1,5

  • 1Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK.

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Summary

This study maps radiomic features to molecular phenotypes in kidney tumors, specifically clear cell renal cell carcinoma (ChRCC) and renal oncocytoma (RO). Integrating imaging and genomic data shows potential for improved differential diagnosis.

Keywords:
chromophobecomputed tomographyoncocytomaradiogenomicsrenal masses

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Area of Science:

  • Radiogenomics
  • Renal Cell Carcinoma Research
  • Oncology Imaging

Background:

  • Renal oncocytoma (RO) and clear cell renal cell carcinoma (ChRCC) are kidney tumors with overlapping imaging characteristics.
  • Accurate differentiation between RO and ChRCC is clinically challenging, impacting treatment decisions.
  • Current diagnostic methods may require invasive procedures.

Purpose of the Study:

  • To develop a radiogenomics map correlating radiomic features from CT scans with molecular phenotypes in RO and ChRCC.
  • To investigate the potential of integrating radiomic and genomic data for improved differential diagnosis of these kidney tumors.

Main Methods:

  • Prospective study including 14 patients (6 RO, 8 ChRCC).
  • Extraction of 1,875 radiomic features from CT scans and genomic data (16,303 genes across 632 cytobands).
  • Application of feature selection algorithms and cross-correlation analysis between radiomic and genomic data.

Main Results:

  • Identification of 13 key radiomic features after selection.
  • Selection of 24 cytobands highly correlated with histology.
  • Discovery of four radiomic features strongly associated with seven genomic features.

Conclusions:

  • The integration of radiomic and genomic data shows promise for enhancing the differential diagnosis of RO and ChRCC.
  • This approach could lead to more precise and non-invasive diagnostic tools for kidney tumors.
  • Radiogenomics offers a pathway towards personalized medicine in renal oncology.