TY - JOUR
T1 - Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer
AU - Lee, Geewon
AU - Bak, So Hyeon
AU - Lee, Ho Yun
AU - Choi, Joon Young
AU - Park, Hyunjin
AU - Lee, Seung Hak
AU - Ohno, Yoshiharu
AU - Nishino, Mizuki
AU - Van Beek, Edwin J.R.
AU - Lee, Kyung Soo
N1 - Publisher Copyright:
© 2019 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2019/3/1
Y1 - 2019/3/1
N2 - 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.
AB - 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.
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U2 - 10.1097/RTI.0000000000000390
DO - 10.1097/RTI.0000000000000390
M3 - Review article
C2 - 30664063
AN - SCOPUS:85060369519
SN - 0883-5993
VL - 34
SP - 103
EP - 115
JO - Journal of Thoracic Imaging
JF - Journal of Thoracic Imaging
IS - 2
ER -