Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects

Abstract

Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.

Keywords: artificial intelligence; genomics; machine learning; radiogenomics; radiomics; renal cancer.

Figure 1

Radiogenomics combining radiomics from imaging features, genomics and clinical data with the purpose of improving clinical decision and patient selection for clinical studies (MRI = magnetic resonance imaging; CT = computed tomography; PET = positron emission tomography; IHC = immunohistochemistry; DNA = deoxyribonucleic acid; LASSO = least absolute shrinkage and selection operator; NGS = next-generation sequencing).