Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects.
Matteo FerroGennaro MusiMichele MarchioniMartina MaggiAlessandro VecciaFrancesco Del GiudiceBiagio BaroneFelice CrocettoFrancesco LasorsaAlessandro AntonelliLuigi SchipsRiccardo AutorinoGian Maria BusettoDaniela TerraccianoGiuseppe LucarelliOctavian Sabin TătaruPublished in: International journal of molecular sciences (2023)
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.
Keyphrases
- artificial intelligence
- end stage renal disease
- clinical trial
- ejection fraction
- chronic kidney disease
- machine learning
- papillary thyroid
- high resolution
- clinical practice
- big data
- prognostic factors
- patient reported outcomes
- palliative care
- single cell
- squamous cell carcinoma
- peritoneal dialysis
- current status
- risk assessment
- lymph node metastasis
- electronic health record
- patient reported
- cross sectional
- contrast enhanced
- phase ii