TY - JOUR
T1 - Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management
T2 - State of the art
AU - Lee, Geewon
AU - Lee, Ho Yun
AU - Park, Hyunjin
AU - Schiebler, Mark L.
AU - van Beek, Edwin J.R.
AU - Ohno, Yoshiharu
AU - Seo, Joon Beom
AU - Leung, Ann
N1 - Publisher Copyright:
© 2016 Elsevier Ireland Ltd
PY - 2017/1/1
Y1 - 2017/1/1
N2 - With the development of functional imaging modalities we now have the ability to study the microenvironment of lung cancer and its genomic instability. Radiomics is defined as the use of automated or semi-automated post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. The automated generation of these analytical features helps to quantify a number of variables in the imaging assessment of lung malignancy. These imaging features include: tumor spatial complexity, elucidation of the tumor genomic heterogeneity and composition, subregional identification in terms of tumor viability or aggressiveness, and response to chemotherapy and/or radiation. Therefore, a radiomic approach can help to reveal unique information about tumor behavior. Currently available radiomic features can be divided into four major classes: (a) morphological, (b) statistical, (c) regional, and (d) model-based. Each category yields quantitative parameters that reflect specific aspects of a tumor. The major challenge is to integrate radiomic data with clinical, pathological, and genomic information to decode the different types of tissue biology. There are many currently available radiomic studies on lung cancer for which there is a need to summarize the current state of the art.
AB - With the development of functional imaging modalities we now have the ability to study the microenvironment of lung cancer and its genomic instability. Radiomics is defined as the use of automated or semi-automated post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. The automated generation of these analytical features helps to quantify a number of variables in the imaging assessment of lung malignancy. These imaging features include: tumor spatial complexity, elucidation of the tumor genomic heterogeneity and composition, subregional identification in terms of tumor viability or aggressiveness, and response to chemotherapy and/or radiation. Therefore, a radiomic approach can help to reveal unique information about tumor behavior. Currently available radiomic features can be divided into four major classes: (a) morphological, (b) statistical, (c) regional, and (d) model-based. Each category yields quantitative parameters that reflect specific aspects of a tumor. The major challenge is to integrate radiomic data with clinical, pathological, and genomic information to decode the different types of tissue biology. There are many currently available radiomic studies on lung cancer for which there is a need to summarize the current state of the art.
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U2 - 10.1016/j.ejrad.2016.09.005
DO - 10.1016/j.ejrad.2016.09.005
M3 - Review article
C2 - 27638103
AN - SCOPUS:84994851692
VL - 86
SP - 297
EP - 307
JO - European Journal of Radiology
JF - European Journal of Radiology
SN - 0720-048X
ER -