Advanced CT Imaging, Radiomics, and Artificial Intelligence to Evaluate Immune Checkpoint Inhibitors’ Effects on Metastatic Renal Cell Carcinoma
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Renal cell carcinoma treatment · Radiomics and Machine Learning in Medical Imaging · Advanced X-ray and CT Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
U.O.C. Diagnostica per Immagini Territoriale Aziendale, Cittadella della Salute Azienda Sanitaria Locale di Lecce, Piazza Filippo Bottazzi, 73100 Lecce, Italy
Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
Department of Health Sciences, Chair of Medical Statistics, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy
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