Immunotherapy Monitoring with Immune Checkpoint Inhibitors Based on [18F]FDG PET/CT in Metastatic Melanomas and Lung Cancer

Abstract

Immunotherapy with checkpoint inhibitors has prompted a major change not only in cancer treatment but also in medical imaging. In parallel with the implementation of new drugs modulating the immune system, new response criteria have been developed, aiming to overcome clinical drawbacks related to the new, unusual, patterns of response characterizing both solid tumors and lymphoma during the course of immunotherapy. The acknowledgement of pseudo-progression, hyper-progression, immune-dissociated response and so forth, has become mandatory for all imagers dealing with this clinical scenario. A long list of acronyms, i.e., irRC, iRECIST, irRECIST, imRECIST, PECRIT, PERCIMT, imPERCIST, iPERCIST, depicts the enormous effort made by radiology and nuclear medicine physicians in the last decade to optimize imaging parameters for better prediction of clinical benefit in immunotherapy regimens. Quite frequently, a combination of clinical-laboratory data with imaging findings has been tested, proving the ability to stratify patients into various risk groups. The next steps necessarily require a large scale validation of the most robust criteria, as well as the clinical implementation of immune-targeting tracers for immuno-PET or the exploitation of radiomics and artificial intelligence as complementary tools during the course of immunotherapy administration. For the present review article, a summary of PET/CT role for immunotherapy monitoring will be provided. By scrolling into various cancer types and applied response criteria, the reader will obtain necessary information for better understanding the potentials and limitations of the modality in the clinical setting.

Emerging Role of FAPI PET Imaging for the Assessment of Benign Bone and Joint Diseases

“…The most common tracer used to perform such a diagnostic procedure is 18 F-fluorodeoxyglucose ([ 18 F]FDG), which has the ability to reveal hypermetabolic lesions based on their increased glycolytic metabolism. As a consequence, [ 18 F]FDG PET/CT has proven its role to assess neoplastic disease both in staging and restaging settings [ 1 , 2 , 3 , 4 , 5 ]. However, its usefulness for the evaluation of inflammatory and infectious diseases has also emerged, in particular in the last decade [ 6 , 7 , 8 , 9 ].…”

The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria

“…In addition, SRE and difference entropy showed the highest predictive power (AUC = 0.90) compared with iRECIST (AUC = 0.79 at 1 month and 0.86 at 4 months) and PD-L1 score (AUC = 0.60). In addition, the advent of immunotherapy has revealed new response patterns, such as hyperprogression, dissociated response, and pseudoprogression [ 35 , 36 , 37 ]. In this setting, Tunali et al [ 37 ] developed a complex predictive model for hyperprogression combining radiomic (approximately 600 features extracted from the largest lung lesions and tumor border regions) and clinical parameters (demographics, mutations, hematologic data) in patients affected by NSCLC treated with single or double ICI.…”

Meditating on Cancer Management at the Time of Immunotherapy

“…In fact, the combination of metabolic information on PET with other clinical and laboratory data can provide useful information in various therapeutic regimens, including checkpoint inhibitors, particularly in tumors such as melanomas and non-small cell lung cancer (NSCLC) [ 7 ]. To better cover the argument, two dedicated reviews [ 9 , 10 ] and two original articles [ 9 , 10 ] have been provided describing the impact of [ 18 F]FDG PET/CT at different timing in both abovementioned tumors. Liberini and colleagues [ 11 ] investigated patients with advanced melanoma undergoing target therapy or immunotherapy and correlated baseline information on PET with tumor response, PFS and OS, proving that poorer outcome was correlated to higher values of whole-body and bone metabolic parameters, whereas OS was correlated to higher values of whole-body, lymph node, and soft tissue metabolic parameters.…”

“…To help better understand the topic, a dedicated article has been prepared by Castello and colleagues [ 10 ], providing a comprehensive review on radiomics in the era of ICI as a new protagonist in the “jungle” of response criteria. By means of this article [ 10 ], together with the last contribution [ 9 ], the Special Issue has been complemented with up-to-date knowledge on advanced metabolic imaging in cancer patients candidate to immunotherapy.…”