4.3 Article

Radiomic and gEnomic approaches for the enhanced DIagnosis of clear cell REnal Cancer (REDIRECt): a translational pilot methodological study

Journal

TRANSLATIONAL ANDROLOGY AND UROLOGY
Volume 11, Issue 2, Pages 149-+

Publisher

AME PUBLISHING COMPANY
DOI: 10.21037/tau-21-713

Keywords

Kidney cancer; renal cancer; genomics; radiomics; transcriptomics

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The study investigated the correlation between imaging features obtained from CT scans and gene expression patterns in ccRCC patients, revealing statistically significant radiogenomic patterns. Despite the high heterogeneity in the transcriptomes of the analyzed samples, the feasibility of the radiogenomic approach was demonstrated, prompting further investigation into its clinical significance.
Background: The combination of radiomic and transcriptomic approaches for patients diagnosed with small clear-cell renal cell carcinoma (ccRCC) might improve decision making. In this pilot and methodological study, we investigate whether imaging features obtained from computed tomography (CT) may correlate with gene expression patterns in ccRCC patients. Methods: Samples from 6 patients who underwent partial nephrectomy for unilateral non-metastatic ccRCC were included in this pilot cohort. Transcriptomic analysis was conducted through RNA-sequencing on tumor samples, while radiologic features were obtained from pre-operative 4-phase contrast-enhanced CT. To evaluate the heterogeneity of the transcriptome, after a 1,000 re-sampling via bootstrapping, a first Principal Component Analyses (PCA) were fitted with all transcripts and a second ones with transcripts deriving from a list of 369 genes known to be associated with ccRCC from The Cancer Genome Atlas (TCGA). Significant pathways in each Principal Components for the 50 genes with the highest loadings absolute values were assessed with pathways enrichment analysis. In addition, Pearson's correlation coefficients among radiomic features themselves and between radiomic features and transcripts expression values were computed. Results: The transcriptomes of the analysed samples showed a high grade of heterogeneity. However, we found four radiogenomic patterns, in which the correlation between radiomic features and transcripts were statistically significant. Conclusions: We showed that radiogenomic approach is feasible, however its clinical meaning should be further investigated.

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