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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
Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
Chintan Parmar (2014)
Computerized characterization of lung nodule subtlety using thoracic CT images.
X. He (2014)
Radiomics: extracting more information from medical images using advanced feature analysis.
P. Lambin (2012)
Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival
B. Ganeshan (2011)
The 7th lung cancer TNM classification and staging system: Review of the changes and implications.
S. Mirsadraee (2012)
Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
Fergus Davnall (2012)
mRMRe: an R package for parallelized mRMR ensemble feature selection
Nicolas De Jay (2013)
N. Dubrawsky (1989)
Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.
Y. Balagurunathan (2014)
Bioconductor: open software development for computational biology and bioinformatics
R. C. Gentleman (2004)
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)
A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.
E. Rios Velazquez (2012)
Tumor volume is a prognostic factor in non-small-cell lung cancer treated with chemoradiotherapy.
B. Alexander (2011)
Characterization of tumor heterogeneity using dynamic contrast enhanced CT and FDG-PET in non-small cell lung cancer.
W. V. van Elmpt (2013)
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Y. Benjamini (1995)
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)
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)
Sequential vs. concurrent chemoradiation for stage III non-small cell lung cancer: randomized phase III trial RTOG 9410.
W. Curran (2011)
Toxicity and response criteria of the Eastern Cooperative Oncology Group
M. Oken (1982)
Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas.
Hee-Dong Chae (2014)
Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability
R. Leijenaar (2013)
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)
Radiomics: the process and the challenges.
Virendra Kumar (2012)
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)
CERR: a computational environment for radiotherapy research.
J. Deasy (2003)
Influence of tumor volume on survival in patients irradiated for non-small-cell lung cancer.
D. Etiz (2002)
survcomp: an R/Bioconductor package for performance assessment and comparison of survival models
Markus S. Schröder (2011)
Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade
K. Skogen (2012)
Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer.
M. Vaidya (2012)
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)
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
Evaluating the yield of medical tests.
F. Harrell (1982)
Cancer statistics , 2014 : cancer Statistics , 2014
R Siegel (2014)
Identification of residual metabolic-active areas within NSCLC tumours using a pre-radiotherapy FDG-PET-CT scan: a prospective validation.
H. Aerts (2012)
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)
Cancer statistics, 2014
R. Siegel (2014)
Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer.
D. Fried (2014)
Predicting outcomes in radiation oncology—multifactorial decision support systems
P. Lambin (2013)
Non-small cell lung cancer: histopathologic correlates for texture parameters at CT.
B. Ganeshan (2013)
Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)
This paper is referenced by
Nomograms for predicting disease progression in patients of Stage I non-small cell lung cancer treated with stereotactic body radiotherapy
L. Ye (2018)
Imaging Biomarker Measurements
B. Beers (2017)
A Quantitative CT Imaging Signature Predicts Survival and Complements Established Prognosticators in Stage I Non-Small Cell Lung Cancer.
Juheon Lee (2018)
Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results
Charlems Alvarez-Jimenez (2020)
Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors
Li-jing Zhang (2020)
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)
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)
MRI heterogeneity analysis for prediction of recurrence and disease free survival in anal cancer.
K. Owczarczyk (2019)
Transdisciplinary Innovation and Future Evidence
Santo Davide Ferrara (2017)
Therapeutic decision based on molecular detection of resistance mechanism in an ALK-rearranged lung cancer patient: a case report
E. De Carlo (2018)
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)
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)
CT texture analysis: a potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib
M. Haider (2017)
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)
Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer
Y. Jiang (2018)
Radiomics and imaging genomics in precision medicine
G. Lee (2017)
Differentiation of focal organising pneumonia and peripheral adenocarcinoma in solid lung lesions using thin-section CT-based radiomics.
T. Zhang (2019)
Radiomic Biomarkers to Refine Risk Models for Distant Metastasis in HPV-related Oropharyngeal Carcinoma.
J. Y. Kwan (2018)
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)
Role of artificial intelligence in integrated analysis of multi-omics and imaging data in cancer research
N. N. Phan (2019)
Potential Application of Radiomics for Differentiating Solitary Pulmonary Nodules
Kaikai Wei (2016)
Radiomics Signature: A Biomarker for the Preoperative Distant Metastatic Prediction of Stage I Nonsmall Cell Lung Cancer.
L. Fan (2018)
Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging
V. Parekh (2020)
Optimize and refine therapeutic index in radiation therapy: Overview of a century.
C. Chargari (2016)
Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures
R. Larue (2017)
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology
Weimiao Wu (2016)
A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme
Jiangwei Lao (2017)
A radiomic approach for adaptive radiotherapy in non-small cell lung cancer patients
S. Ramella (2018)
Radiomics in stratification of pancreatic cystic lesions: Machine learning in action.
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