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CT-based Radiomic Signature Predicts Distant Metastasis In Lung Adenocarcinoma.

T. Coroller, P. Grossmann, Y. Hou, E. Rios Velazquez, R. Leijenaar, Gretchen Hermann, P. Lambin, Benjamin Haibe-Kains, R. Mak, H. Aerts
Published 2015 · Medicine

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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=2.77×10(-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.
This paper references
10.1371/journal.pone.0102107
Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
Chintan Parmar (2014)
10.1088/0031-9155/59/4/897
Computerized characterization of lung nodule subtlety using thoracic CT images.
X. He (2014)
10.1016/j.ejca.2011.11.036
Radiomics: extracting more information from medical images using advanced feature analysis.
P. Lambin (2012)
10.1007/s00330-011-2319-8
Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival
B. Ganeshan (2011)
10.4329/wjr.v4.i4.128
The 7th lung cancer TNM classification and staging system: Review of the changes and implications.
S. Mirsadraee (2012)
10.1007/s13244-012-0196-6
Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
Fergus Davnall (2012)
10.1093/bioinformatics/btt383
mRMRe: an R package for parallelized mRMR ensemble feature selection
Nicolas De Jay (2013)
10.3322/canjclin.39.6.399
Cancer statistics
N. Dubrawsky (1989)
10.1593/TLO.13844
Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.
Y. Balagurunathan (2014)
10.1186/gb-2004-5-10-r80
Bioconductor: open software development for computational biology and bioinformatics
R. C. Gentleman (2004)
10.1200/JCO.2007.14.4824
Phase III trial of maintenance gefitinib or placebo after concurrent chemoradiotherapy and docetaxel consolidation in inoperable stage III non-small-cell lung cancer: SWOG S0023.
K. Kelly (2008)
10.1016/j.radonc.2012.09.023
A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.
E. Rios Velazquez (2012)
10.1016/j.ijrobp.2009.12.060
Tumor volume is a prognostic factor in non-small-cell lung cancer treated with chemoradiotherapy.
B. Alexander (2011)
10.1016/j.radonc.2013.08.032
Characterization of tumor heterogeneity using dynamic contrast enhanced CT and FDG-PET in non-small cell lung cancer.
W. V. van Elmpt (2013)
10.1111/J.2517-6161.1995.TB02031.X
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Y. Benjamini (1995)
10.1007/s00330-013-2965-0
Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy
M. Ravanelli (2013)
10.1038/ncomms5006
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)
10.1093/jnci/djr325
Sequential vs. concurrent chemoradiation for stage III non-small cell lung cancer: randomized phase III trial RTOG 9410.
W. Curran (2011)
10.1097/00000421-198212000-00014
Toxicity and response criteria of the Eastern Cooperative Oncology Group
M. Oken (1982)
10.1148/radiol.14132187
Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas.
Hee-Dong Chae (2014)
10.3109/0284186X.2013.812798
Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability
R. Leijenaar (2013)
10.1016/S0140-6736(09)60737-6
Radiotherapy plus chemotherapy with or without surgical resection for stage III non-small-cell lung cancer: a phase III randomised controlled trial
K. Albain (2009)
10.1016/j.mri.2012.06.010
Radiomics: the process and the challenges.
Virendra Kumar (2012)
10.1097/JTO.0B013E318268FF90
The Complex Relationship Between Lung Tumor Volume and Survival in Patients with Non-Small Cell Lung Cancer Treated by Definitive Radiotherapy: a Prospective, Observational Prognostic Factor Study of the Trans-Tasman Radiation Oncology Group (Trog 99.05)
D. Ball (2012)
10.1118/1.1568978
CERR: a computational environment for radiotherapy research.
J. Deasy (2003)
10.1016/S0360-3016(02)02814-6
Influence of tumor volume on survival in patients irradiated for non-small-cell lung cancer.
D. Etiz (2002)
10.1093/bioinformatics/btr511
survcomp: an R/Bioconductor package for performance assessment and comparison of survival models
Markus S. Schröder (2011)
10.1007/s11060-012-1010-5
Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade
K. Skogen (2012)
10.1016/j.radonc.2011.10.014
Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer.
M. Vaidya (2012)
10.1200/JCO.2008.17.7840
Phase III study of cisplatin, etoposide, and concurrent chest radiation with or without consolidation docetaxel in patients with inoperable stage III non-small-cell lung cancer: the Hoosier Oncology Group and U.S. Oncology.
N. Hanna (2008)
10.1102/1470-7330.2010.0021
Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage
B. Ganeshan (2010)
Non_Small Cell Lung Cancer: Histopathologic Correlates for Texture Parameters at CT. Radiology
B Ganeshan
10.1001/JAMA.1982.03320430047030
Evaluating the yield of medical tests.
F. Harrell (1982)
Cancer statistics , 2014 : cancer Statistics , 2014
R Siegel (2014)
10.1016/j.lungcan.2011.06.003
Identification of residual metabolic-active areas within NSCLC tumours using a pre-radiotherapy FDG-PET-CT scan: a prospective validation.
H. Aerts (2012)
10.1200/JCO.2013.31.15_SUPPL.7501
A randomized phase III comparison of standard-dose (60 Gy) versus high-dose (74 Gy) conformal chemoradiotherapy with or without cetuximab for stage III non-small cell lung cancer: Results on radiation dose in RTOG 0617.
J. Bradley (2013)
R: A language and environment for statistical computing.
R. Team (2014)
10.3322/caac.21208
Cancer statistics, 2014
R. Siegel (2014)
10.1016/j.ijrobp.2014.07.020
Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer.
D. Fried (2014)
10.1038/nrclinonc.2012.196
Predicting outcomes in radiation oncology—multifactorial decision support systems
P. Lambin (2013)
10.1148/radiol.12112428
Non-small cell lung cancer: histopathologic correlates for texture parameters at CT.
B. Ganeshan (2013)
10.1038/NCOMMS5644
Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)



This paper is referenced by
10.1093/jjco/hyx179
Nomograms for predicting disease progression in patients of Stage I non-small cell lung cancer treated with stereotactic body radiotherapy
L. Ye (2018)
10.1007/978-3-319-43504-6_8
Imaging Biomarker Measurements
B. Beers (2017)
10.1016/j.ijrobp.2018.01.006
A Quantitative CT Imaging Signature Predicts Survival and Complements Established Prognosticators in Stage I Non-Small Cell Lung Cancer.
Juheon Lee (2018)
10.3390/cancers12123663
Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results
Charlems Alvarez-Jimenez (2020)
10.1007/s11547-020-01138-6
Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors
Li-jing Zhang (2020)
10.1080/0284186X.2017.1351624
Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study
R. Larue (2017)
Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty
Rebecca Nichole Mahon (2018)
10.1038/s41598-018-20713-6
Effect of tube current on computed tomography radiomic features
D. Mackin (2018)
Establishment of a new non-invasive imaging prediction model for liver metastasis in colon cancer.
Y. Li (2019)
10.1016/J.RADONC.2019.01.022
MRI heterogeneity analysis for prediction of recurrence and disease free survival in anal cancer.
K. Owczarczyk (2019)
10.1007/978-3-319-67092-8_39
Transdisciplinary Innovation and Future Evidence
Santo Davide Ferrara (2017)
10.2147/OTT.S184745
Therapeutic decision based on molecular detection of resistance mechanism in an ALK-rearranged lung cancer patient: a case report
E. De Carlo (2018)
10.1007/s00330-019-06213-9
Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study
Wei Wu (2019)
10.1038/srep43356
Associations between Tumor Vascularity, Vascular Endothelial Growth Factor Expression and PET/MRI Radiomic Signatures in Primary Clear-Cell–Renal-Cell-Carcinoma: Proof-of-Concept Study
Qingbo Yin (2017)
10.1186/s40644-017-0106-8
CT texture analysis: a potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib
M. Haider (2017)
10.3892/etm.2019.7357
Application of computed tomography-based radiomics signature analysis in the prediction of the response of small cell lung cancer patients to first-line chemotherapy
H. Wei (2019)
10.1016/j.ebiom.2018.09.007
Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer
Y. Jiang (2018)
10.23838/PFM.2017.00101
Radiomics and imaging genomics in precision medicine
G. Lee (2017)
10.1016/j.crad.2018.08.014
Differentiation of focal organising pneumonia and peripheral adenocarcinoma in solid lung lesions using thin-section CT-based radiomics.
T. Zhang (2019)
10.1016/j.ijrobp.2018.01.057
Radiomic Biomarkers to Refine Risk Models for Distant Metastasis in HPV-related Oropharyngeal Carcinoma.
J. Y. Kwan (2018)
10.1016/j.patcog.2018.11.011
Random forest dissimilarity based multi-view learning for Radiomics application
Hongliu Cao (2019)
Characterization of Computed Tomography Radiomic Features using Texture Phantoms
Muhammad Shafiq ul Hassan (2018)
10.21037/tcr.2019.12.17
Role of artificial intelligence in integrated analysis of multi-omics and imaging data in cancer research
N. N. Phan (2019)
10.4172/2167-7964.1000218
Potential Application of Radiomics for Differentiating Solitary Pulmonary Nodules
Kaikai Wei (2016)
10.1016/j.acra.2018.11.004
Radiomics Signature: A Biomarker for the Preoperative Distant Metastatic Prediction of Stage I Nonsmall Cell Lung Cancer.
L. Fan (2018)
10.1007/s10549-020-05533-5
Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging
V. Parekh (2020)
10.1016/j.ctrv.2016.03.001
Optimize and refine therapeutic index in radiation therapy: Overview of a century.
C. Chargari (2016)
10.1259/bjr.20160665
Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures
R. Larue (2017)
10.3389/fonc.2016.00071
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology
Weimiao Wu (2016)
10.1038/s41598-017-10649-8
A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme
Jiangwei Lao (2017)
10.1371/journal.pone.0207455
A radiomic approach for adaptive radiotherapy in non-small cell lung cancer patients
S. Ramella (2018)
10.1016/j.canlet.2019.10.023
Radiomics in stratification of pancreatic cystic lesions: Machine learning in action.
V. Dalal (2019)
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