PET Radiomics and Response to Immunotherapy in Lung Cancer: A Systematic Review of the Literature

The present review was performed in order to provide a comprehensive overview of the existing literature concerning the applications of positron emission tomography (PET) radiomics in lung cancer patients candidates or those currently undergoing immunotherapy. Fifteen papers were included, thirteen were qualified as using conventional radiomics approaches, and two used deep learning radiomics. Different settings were analyzed, from the utility of radiomics as an additional tool for predicting the expression of PD-L1 or the tumor microenvironment, to the utility of artificial intelligence in evaluating the response to immunotherapy. Although radiomics seems promising in these fields, too limited data are now available. Indeed, the first limitation is the low amount of data, heterogeneity in the provided information, the still limited experience and also the small amount of expertise in this field. Therefore, radiomics is still far from to be considered for daily routine clinical practice, although some additional efforts are required for the next future, mainly in patients scheduled or undergoing immunotherapy.Abstract: The aim of this review is to provide a comprehensive overview of the existing literature concerning the applications of positron emission tomography (PET) radiomics in lung cancer patient candidates or those undergoing immunotherapy. Materials and Methods: A systematic review was conducted on databases and web sources. English-language original articles were considered. The title and abstract were independently reviewed to evaluate study inclusion. Duplicate, out-of-topic, and review papers, or editorials, articles, and letters to editors were excluded. For each study, the radiomics analysis was assessed based on the radiomics quality score (RQS 2.0). The review was registered on the PROSPERO database with the number CRD42023402302. Results: Fifteen papers were included, thirteen were qualified as using conventional radiomics approaches, and two used deep learning radiomics. The content of each study was different; indeed, seven papers investigated the potential ability of radiomics to predict PD-L1 expression and tumor microenvironment before starting immunotherapy. Moreover, two evaluated the prediction of response, and four investigated the utility of radiomics to predict the response to immunotherapy. Finally, two papers investigated the prediction of adverse events due to immunotherapy. Conclusions: Radiomics is promising for the evaluation of TME and for the prediction of response to immunotherapy, but some limitations should be overcome.