Advanced CT Imaging, Radiomics, and Artificial Intelligence to Evaluate Immune Checkpoint Inhibitors’ Effects on Metastatic Renal Cell Carcinoma

Abstract

Advances in the knowledge of renal cell carcinoma (RCC)’s oncogenesis have led to the development of new therapeutic approaches, such as immune checkpoint inhibitors (ICIs), which have improved the clinical outcomes of metastatic RCC (mRCC) patients. Our literature search led to a series of studies that were divided into four subcategories: RECIST criteria, radiomics and artificial intelligence, atypical response patterns, and body composition. These studies provide novel and promising data aimed at improving patient management and clinical outcomes, further strengthening the concept of precision medicine. Radiomics and artificial intelligence allow us to obtain—in a non-invasive fashion—a multitude of data that cannot be detected with the naked eye, offering potential advantages that might help to predict the response to treatments and possibly improve patients’ outcomes through a personalized therapeutic approach. The purpose of this literature review is to describe the available evidence on the role of computed tomography (CT) in evaluating and predicting ICIs’ effects on mRCC patients by applying radiomics and artificial intelligence.

Keywords

• artificial intelligence
• atypical response patterns
• body composition
• computed tomography
• CT texture analysis
• immune checkpoint inhibitors