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Potentials of Non-Invasive 18F-FDG PET/CT in Immunotherapy Prediction for Non–Small Cell Lung Cancer

Xuhe Liao, Meng Liu, Rongfu Wang, Jianhua Zhang

2022Frontiers in Genetics15 citationsDOIOpen Access PDF

Abstract

The immune checkpoint inhibitors (ICIs), by targeting cytotoxic-T-lymphocyte-associated protein 4, programmed cell death 1 (PD-1), or PD-ligand 1, have dramatically changed the natural history of several cancers, including non–small cell lung cancer (NSCLC). There are unusual response manifestations (such as pseudo-progression, hyper-progression, and immune-related adverse events) observed in patients with ICIs because of the unique mechanisms of these agents. These specific situations challenge response and prognostic assessment to ICIs challenging. This review demonstrates how 18 F-FDG PET/CT can help identify these unusual response patterns in a non-invasive and effective way. Then, a series of semi-quantitative parameters derived from 18 F-FDG PET/CT are introduced. These indexes have been recognized as the non-invasive biomarkers to predicting the efficacy of ICIs and survival of NSCLC patients according to the latest clinical studies. Moreover, the current situation regarding the functional criteria based on 18 F-FDG PET/CT for immunotherapeutic response assessment is presented and analyzed. Although the criteria based on 18 F-FDG PET/CT proposed some resolutions to overcome limitations of morphologic criteria in the assessment of tumor response to ICIs, further researches should be performed to validate and improve these assessing systems. Then, the last part in this review displays the present status and a perspective of novel specific PET probes targeting key molecules relevant to immunotherapy in prediction and response assessment.

Topics & Concepts

MedicineImmunotherapyLung cancerCancerImmune systemOncologyBiomarkerCancer immunotherapyPositron emission tomographyInternal medicineImmunologyRadiologyBiologyBiochemistryCancer Immunotherapy and BiomarkersMedical Imaging Techniques and ApplicationsRadiomics and Machine Learning in Medical Imaging
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