TY - JOUR
T1 - Radiomics applied to lung cancer: a review
AU - Scrivener, Madeleine
AU - de Jong, Evelyn E. C.
AU - van Timmeren, Janita
AU - Pieters, Thierry
AU - Ghaye, Benoit
AU - Geets, Xavier
PY - 2016/8
Y1 - 2016/8
N2 - Lung cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics, a concept introduced in 2012, refers to the comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features (watch the animation: https://youtu.be/Tq980GEVP0Y and the website www.radiomics.org). Here, we review the literature related to radiomics for lung cancer. We found 11 papers related to computed tomography (CT) radiomics, 3 to radiomics or texture analysis with positron emission tomography (PET) and 8 relating to PET/CT radiomics. There are two main applications of radiomics, the classification of lung nodules (diagnostic) or prognostication of established lung cancer (theragnostic). There are quite a few methodological issues in most of the reviewed papers. Only 5 studies, out of the 22, were externally validated. Overall, it is clear that radiomics offers great potential in improving diagnosis and patient stratification in lung cancer. It may also have a real clinical impact, as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision support in lung cancer treatment at low cost.
AB - Lung cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics, a concept introduced in 2012, refers to the comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features (watch the animation: https://youtu.be/Tq980GEVP0Y and the website www.radiomics.org). Here, we review the literature related to radiomics for lung cancer. We found 11 papers related to computed tomography (CT) radiomics, 3 to radiomics or texture analysis with positron emission tomography (PET) and 8 relating to PET/CT radiomics. There are two main applications of radiomics, the classification of lung nodules (diagnostic) or prognostication of established lung cancer (theragnostic). There are quite a few methodological issues in most of the reviewed papers. Only 5 studies, out of the 22, were externally validated. Overall, it is clear that radiomics offers great potential in improving diagnosis and patient stratification in lung cancer. It may also have a real clinical impact, as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision support in lung cancer treatment at low cost.
KW - Lung cancer
KW - imaging
KW - radiomics
KW - theragnostic
U2 - 10.21037/tcr.2016.06.18
DO - 10.21037/tcr.2016.06.18
M3 - Article
SN - 2218-676X
VL - 5
SP - 398
EP - 409
JO - Translational Cancer Research
JF - Translational Cancer Research
IS - 4
ER -