Litcius/Paper detail

Imaging of Cancer Immunotherapy: Response Assessment Methods, Atypical Response Patterns, and Immune-Related Adverse Events, From the<i>AJR</i>Special Series on Imaging of Inflammation

Sara Sheikhbahaei, Charles V. Marcus, Mohammad S. Sadaghiani, Steven P. Rowe, Martin G. Pomper, Lilja B. Solnes

2021American Journal of Roentgenology19 citationsDOI

Abstract

The introduction of immunotherapy with immune-checkpoint inhibitors (ICIs) has revolutionized cancer treatment paradigms. Since FDA approval of the first ICI in 2011, multiple additional ICIs have been approved and granted marketing authorization, and many promising agents are in early clinical adoption. Due to the distinctive biologic mechanisms of ICIs, the patterns of tumor response and progression seen with immunotherapy differ from those observed with cytotoxic chemothera-pies. With increasing clinical adoption of immunotherapy, it is critical for radiologists to recognize different response patterns and common pitfalls to avoid misinterpretation of imaging studies or prompt premature cessation of potentially effective treatment. This review provides an overview of ICIs and their mechanisms of action and discusses anatomic and metabolic immune-related response assessment methods, typical and atypical patterns of immunotherapy response (including pseudoprogression, hyperprogression, dissociated response, and durable response), and common imaging features of immune-related adverse events. Future multicenter trials are needed to validate the proposed immune-related response criteria and identify the functional imaging markers of early treatment response and survival.

Topics & Concepts

MedicineImmunotherapyClinical trialCancerAdverse effectOncologyComplete responseInflammatory responseIntensive care medicineInternal medicineInflammationCancer immunotherapyBioinformaticsBiomarkerPathologyFunctional imagingResponse Evaluation Criteria in Solid TumorsMedical imagingImmunologyCancer Immunotherapy and BiomarkersInflammatory Biomarkers in Disease PrognosisRadiomics and Machine Learning in Medical Imaging