CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.
OBJECTIVE
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.


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=2.77×10(-5)) in the discovery cohort. A radiomic-signature had strong power for predicting DM in the independent validation dataset (CI=0.61, p-value=1.79×10(-17)). Adding this radiomic-signature to a clinical model resulted in a significant improvement of predicting DM in the validation dataset (p-value=1.56×10(-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.
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiation Oncology (MAASTRO), GROW Research Institute, Maastricht University, The Netherlands. Electronic address: tcoroller@lroc.harvard.edu.
Thibaud P
TP
Coroller
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiation Oncology (MAASTRO), GROW Research Institute, Maastricht University, The Netherlands.
Patrick
P
Grossmann
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Ying
Y
Hou
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Emmanuel
E
Rios Velazquez
Department of Radiation Oncology (MAASTRO), GROW Research Institute, Maastricht University, The Netherlands.
Ralph T H
RT
Leijenaar
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Gretchen
G
Hermann
Department of Radiation Oncology (MAASTRO), GROW Research Institute, Maastricht University, The Netherlands.
Philippe
P
Lambin
Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Medical Biophysics Department, University of Toronto, Canada.
Benjamin
B
Haibe-Kains
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Raymond H
RH
Mak
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiation Oncology (MAASTRO), GROW Research Institute, Maastricht University, The Netherlands. Electronic address: Hugo_Aerts@dfci.harvard.edu.
Hugo J W L
HJ
Aerts
D000230:Adenocarcinoma / Q000000981:diagnostic imaging*; D000077192:Adenocarcinoma of Lung; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D009362:Neoplasm Metastasis; D011379:Prognosis; D014057:Tomography, X-Ray Computed / Q000379:methods; D047368:Tumor Burden
Biomarkers; Distant metastasis; Lung adenocarcinoma; NSCLC; Quantitative imaging; Radiomics