TY - JOUR
T1 - CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
AU - Coroller, Thibaud P.
AU - Grossmann, Patrick
AU - Hou, Ying
AU - Velazquez, Emmanuel Rios
AU - Leijenaar, Ralph T. H.
AU - Hermann, Gretchen
AU - Lambin, Philippe
AU - Haibe-Kains, Benjamin
AU - Mak, Raymond H.
AU - Aerts, Hugo J. W. L.
PY - 2015/3
Y1 - 2015/3
N2 - Background and purpose: Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients. Material and methods: We included two datasets: 98 patients for discovery and 84 for validation. The phenotype of the primary tumor was quantified on pre-treatment CT-scans using 635 radiomic features. Univariate and multivariate analysis was performed to evaluate radiomics performance using the concordance index (CI). Results: Thirty-five radiomic features were found to be prognostic (CI > 0.60, FDR <5%) for DM and twelve for survival. It is noteworthy that tumor volume was only moderately prognostic for DM (CI = 0.55, p-value = 2.77 x 10(-5)) in the discovery cohort. A radiomic-signature had strong power for predicting DM in the independent validation dataset (CI = 0.61, p-value = 1.79 x 10(-17)). Adding this radiomic-signature to a clinical model resulted in a significant improvement of predicting DM in the validation dataset (p-value = 1.56 x 10(-11)). Conclusions: Although only basic metrics are routinely quantified, this study shows that radiomic features capturing detailed information of the tumor phenotype can be used as a prognostic biomarker for clinically-relevant factors such as DM. Moreover, the radiomic-signature provided additional information to clinical data.
AB - Background and purpose: Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients. Material and methods: We included two datasets: 98 patients for discovery and 84 for validation. The phenotype of the primary tumor was quantified on pre-treatment CT-scans using 635 radiomic features. Univariate and multivariate analysis was performed to evaluate radiomics performance using the concordance index (CI). Results: Thirty-five radiomic features were found to be prognostic (CI > 0.60, FDR <5%) for DM and twelve for survival. It is noteworthy that tumor volume was only moderately prognostic for DM (CI = 0.55, p-value = 2.77 x 10(-5)) in the discovery cohort. A radiomic-signature had strong power for predicting DM in the independent validation dataset (CI = 0.61, p-value = 1.79 x 10(-17)). Adding this radiomic-signature to a clinical model resulted in a significant improvement of predicting DM in the validation dataset (p-value = 1.56 x 10(-11)). Conclusions: Although only basic metrics are routinely quantified, this study shows that radiomic features capturing detailed information of the tumor phenotype can be used as a prognostic biomarker for clinically-relevant factors such as DM. Moreover, the radiomic-signature provided additional information to clinical data.
KW - Radiomics
KW - Lung adenocarcinoma
KW - NSCLC
KW - Quantitative imaging
KW - Biomarkers
KW - Distant metastasis
U2 - 10.1016/j.radonc.2015.02.015
DO - 10.1016/j.radonc.2015.02.015
M3 - Article
SN - 0167-8140
VL - 114
SP - 345
EP - 350
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
IS - 3
ER -