Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non–small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics.
Departments of *Radiology and Center for Imaging Science
§Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul
†Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan
‡Department of Radiology, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon
∥School of Electronic and Electrical Engineering, Sungkyunkwan University
¶Center for Neuroscience Imaging Research, Institute for Basic Science
#Department of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea
**Department of Radiology, Division of Functional and Diagnostic Imaging Research, Kobe University Graduate School of Medicine
††Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
‡‡Department of Radiology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Boston, MA
§§Edinburgh Imaging, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK
Geewon Lee and So Hyeon Bak contributed equally to this work.
Supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, which was funded by the Ministry of Health & Welfare (HI17C0086) and by a National Research Foundation of Korea grant funded by the Korean government (Ministry of Science, ICT, & Future Planning) (No. NRF-2016R1A2B4013046 and NRF-2017M2A2A7A02018568).
Mizuki Nishino is consultant to Toshiba Medical Systems, WorldCare Clinical, Daiichi Sankyo; Research grant to the institution from Merck Investigator Studies Program, Toshiba Medical Systems, AstraZeneca; Honorarium from Bayer, Roche. Edwin J.R. van Beek is owner/founder of Quantitative Clinical Trials Imaging Services Ltd. Yoshiharu Ohno has a research grant from Canon Medical Systems Corporation. The remaining authors declare no conflicts of interest.
Correspondence to: Ho Yun Lee, PhD, Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Gangnam-Gu, Seoul 135-710, Korea (e-mail: firstname.lastname@example.org).