ArticleTranslational andrology and urology2022
Radiomic and gEnomic approaches for the enhanced DIagnosis of clear cell REnal Cancer (REDIRECt): a translational pilot methodological study.
Article in Translational andrology and urology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 6 citations in OpenAlex.
- The role of radiomics in predicting the response to neoadjuvant chemotherapy for breast cancer.Cancer biology & medicine · 2026Review
- Regionally modulated radiomics analysis in PET/CT imaging: application to prognosis prediction of head and neck cancer.Physical and engineering sciences in medicine · 2026Article
- Value of radiomics models in precision diagnosis of dual-phenotype hepatocellular carcinoma and intrahepatic cholangiocarcinoma.World journal of radiology · 2025Article
- Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects.International journal of molecular sciences · 2023Review
- Artificial intelligence and radiomics applications in adrenal lesions: a systematic review.Therapeutic advances in urologyReview
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Authors and funding
23 authors at 3 institutions in 1 country.
Funding
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Abstract
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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