Radiomics for Renal Cell Carcinoma: Predicting Outcomes from Immunotherapy and Targeted Therapies-A Narrative Review

Abstract

T-cell immunotherapy and molecular targeted therapies have become standard-of-care treatments for renal cell carcinoma (RCC). There is a need to develop robust biomarkers that predict patient outcomes to targeted therapies to personalise treatment. In recent years, quantitative analysis of imaging features, termed radiomics, has been used to extract tumour features. This narrative mini review summarises the evidence for radiomics prediction of immunotherapy and molecular targeted therapy outcomes in RCC. Radiomics may predict survival, treatment response, and disease progression in RCC treated with tyrosine kinase inhibitors (eg, sunitinib) and immune checkpoint inhibitors (eg, nivolumab). Further validation is necessary in large-scale studies. PATIENT SUMMARY: We summarise evidence on the ability of features extracted from CT (computed tomography) scans to predict patient outcomes from new treatments for kidney cancer. Although these features can predict treatment outcomes for patients, including survival, treatment response, and cancer progression, further research is necessary before this technology can be applied clinically.

Keywords: Artificial intelligence; Immunotherapy; Kidney cancer; Machine learning; Molecular targeted therapy; Precision medicine; Radiomics; Renal cell carcinoma; Review.