Multimodal Imaging Approach for Tumor Treatment Response Evaluation in the Era of Immunotherapy

Geewon Lee1, Seung Hwan Moon, Jong Hoon Kim

  • 1From the Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (G.L., D.Y.J., J.C., H.Y.L.); Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, South Korea (G.L.); Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (S.H.M., J.Y.C.); Industrial Biomaterial Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, South Korea (J.H.K.); Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea (J.C.); and Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea (H.Y.L.).

PubMed

Immunotherapy is likely the most remarkable advancement in lung cancer treatment during the past decade. Although immunotherapy provides substantial benefits, their therapeutic responses differ from those of conventional chemotherapy and targeted therapy, and some patients present unique immunotherapy response patterns that cannot be judged under the current measurement standards. Therefore, the response monitoring of immunotherapy can be challenging, such as the differentiation between real response and pseudo-response. This review outlines the various tumor response patterns to immunotherapy and discusses methods for quantifying computed tomography (CT) and 18 F-fluorodeoxyglucose positron emission tomography (PET) in the field of lung cancer. Emerging technologies in magnetic resonance imaging (MRI) and non-FDG PET tracers are also explored. With immunotherapy responses, the role for imaging is essential in both anatomical radiological responses (CT/MRI) and molecular changes (PET imaging). Multiple aspects must be considered when assessing treatment responses using CT and PET. Finally, we introduce multimodal approaches that integrate imaging and nonimaging data, and we discuss future directions for the assessment and prediction of lung cancer responses to immunotherapy.

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