Proper patient selection for immunotherapy is critical, as certain tumor microenvironments are more permissible to therapy than others. Currently, the use of programmed cell death ligand-1 (PD-L1) and microsatellite instability high and/or mismatch repair deficiency are used as biomarkers for immunotherapy response. To improve tumor characterization, methodologies are being developed to combine imaging with tumor immune environment characterization. Imaging of tumors from immunotherapy responders and nonresponders with various imaging modalities has led to the development of criteria that could predict patient response to immunotherapy. Additionally, radiomics-based artificial intelligence methods are being used to characterize tumor microenvironments to predict and evaluate immunotherapy responses, as well as to predict risk of immune-related adverse events. Molecular imaging techniques are also being developed for various modalities to observe tumor expression of immunotherapy targets, such as PD-L1 and, to confirm the target is being expressed on resident tumors. In all, the advancements of imaging techniques to define tumor immunologic characteristics will help to stratify patients who are more likely to respond to immunotherapies. Keywords: Computer Aided Diagnosis (CAD), Computer Applications-Virtual Imaging, Efficacy Studies, MR-Imaging, Molecular Imaging-Cancer, Molecular Imaging-Immunotherapy, Molecular Imaging-Nanoparticles, Molecular Imaging-Probe Development, Molecular Imaging-Target Development, SPECT/CT © RSNA, 2019.
2019 by the Radiological Society of North America, Inc.