CT Slice Thickness and Convolution Kernel Affect Performance of a Radiomic Model for Predicting EGFR Status in Non-Small Cell Lung Cancer: A Preliminary Study

[1]  Lin Lu,et al.  Impact of Variability in Portal Venous Phase Acquisition Timing in Tumor Density Measurement and Treatment Response Assessment: Metastatic Colorectal Cancer as a Paradigm. , 2017, JCO clinical cancer informatics.

[2]  Lin Lu,et al.  Interobserver variability in tumor contouring affects the use of radiomics to predict mutational status , 2017, Journal of medical imaging.

[3]  J. Soria,et al.  Limits of radiomic-based entropy as a surrogate of tumor heterogeneity: ROI-area, acquisition protocol and tissue site exert substantial influence , 2017, Scientific Reports.

[4]  Yongwei Yu,et al.  Radiological and Clinical Features associated with Epidermal Growth Factor Receptor Mutation Status of Exon 19 and 21 in Lung Adenocarcinoma , 2017, Scientific Reports.

[5]  Lawrence H. Schwartz,et al.  Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings , 2016, PloS one.

[6]  F. Galateau-Sallé,et al.  The IASLC Mesothelioma Staging Project: Proposals for the M Descriptors and for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Mesothelioma , 2016, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[7]  Zaiyi Liu,et al.  Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule , 2016, Scientific Reports.

[8]  L. Schwartz,et al.  Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC , 2016, Scientific Reports.

[9]  Y. Liu,et al.  Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas. , 2016, Clinical lung cancer.

[10]  Sae Rom Hong,et al.  Predictors of False-Negative Results from Percutaneous Transthoracic Fine-Needle Aspiration Biopsy: An Observational Study from a Retrospective Cohort , 2016, Yonsei medical journal.

[11]  W. Tsai,et al.  Reproducibility of radiomics for deciphering tumor phenotype with imaging , 2016, Scientific Reports.

[12]  Ying Liu,et al.  CT Features Associated with Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma. , 2016, Radiology.

[13]  Tae Jung Kim,et al.  Radiologic Characteristics of Surgically Resected Non-Small Cell Lung Cancer With ALK Rearrangement or EGFR Mutations. , 2016, The Annals of thoracic surgery.

[14]  J. Crowley,et al.  The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer , 2016, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[15]  Richard D. White,et al.  CT Gray-Level Texture Analysis as a Quantitative Imaging Biomarker of Epidermal Growth Factor Receptor Mutation Status in Adenocarcinoma of the Lung. , 2015, AJR. American journal of roentgenology.

[16]  R. McCormack,et al.  EGFR mutation incidence in non-small-cell lung cancer of adenocarcinoma histology: a systematic review and global map by ethnicity (mutMapII). , 2015, American journal of cancer research.

[17]  Ming Liu,et al.  EGFR L858R mutation is associated with lung adenocarcinoma patients with dominant ground-glass opacity. , 2015, Lung cancer.

[18]  Y. Shim,et al.  Lung cancer in never-smoker Asian females is driven by oncogenic mutations, most often involving EGFR , 2015, Oncotarget.

[19]  X. Chen,et al.  Comparative analysis of clinicoradiologic characteristics of lung adenocarcinomas with ALK rearrangements or EGFR mutations , 2015, European Radiology.

[20]  Mi Young Kim,et al.  Advanced adenocarcinoma of the lung: comparison of CT characteristics of patients with anaplastic lymphoma kinase gene rearrangement and those with epidermal growth factor receptor mutation. , 2015, Radiology.

[21]  G. Liu,et al.  Correlation Between EGFR Mutation Status and Computed Tomography Features in Patients With Advanced Pulmonary Adenocarcinoma , 2014, Journal of thoracic imaging.

[22]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[23]  Shing M. Lee,et al.  Variability in Assessing Treatment Response: Metastatic Colorectal Cancer as a Paradigm , 2014, Clinical Cancer Research.

[24]  Peter J. Ell,et al.  Multifunctional Imaging Signature for V-KI-RAS2 Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) Mutations in Colorectal Cancer , 2014, The Journal of Nuclear Medicine.

[25]  Lawrence H. Schwartz,et al.  Epidermal growth factor receptor mutation in lung adenocarcinomas: relationship with CT characteristics and histologic subtypes. , 2013, Radiology.

[26]  D. Aberle,et al.  Results of initial low-dose computed tomographic screening for lung cancer. , 2013, The New England journal of medicine.

[27]  Kenji Suzuki,et al.  The size of consolidation on thin-section computed tomography is a better predictor of survival than the maximum tumour dimension in resectable lung cancer. , 2013, European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.

[28]  L. Schwartz,et al.  Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field. , 2013, Medical physics.

[29]  Ahmedin Jemal,et al.  Annual number of lung cancer deaths potentially avertable by screening in the United States , 2013, Cancer.

[30]  Vicky Goh,et al.  Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? , 2013, European journal of radiology.

[31]  K. Miles,et al.  Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival , 2012, European Radiology.

[32]  M. Okada,et al.  Prognostic significance of using solid versus whole tumor size on high-resolution computed tomography for predicting pathologic malignant grade of tumors in clinical stage IA lung adenocarcinoma: a multicenter study. , 2012, The Journal of thoracic and cardiovascular surgery.

[33]  K. Miles,et al.  Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. , 2012, Clinical radiology.

[34]  C. Gatsonis,et al.  Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .

[35]  Akihiko Yoshizawa,et al.  Impact of proposed IASLC/ATS/ERS classification of lung adenocarcinoma: prognostic subgroups and implications for further revision of staging based on analysis of 514 stage I cases , 2011, Modern Pathology.

[36]  G. Veronesi,et al.  Risk factors for complications of CT-guided lung biopsies , 2011, La radiologia medica.

[37]  Balaji Ganeshan,et al.  Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage , 2010, Cancer imaging : the official publication of the International Cancer Imaging Society.

[38]  T. Mok,et al.  Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. , 2009, The New England journal of medicine.

[39]  Mariano Provencio,et al.  Screening for epidermal growth factor receptor mutations in lung cancer. , 2009, The New England journal of medicine.

[40]  Gregory J Riely,et al.  Impact of Epidermal Growth Factor Receptor and KRAS Mutations on Clinical Outcomes in Previously Untreated Non–Small Cell Lung Cancer Patients: Results of an Online Tumor Registry of Clinical Trials , 2009, Clinical Cancer Research.

[41]  Binsheng Zhao,et al.  Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer. , 2009, Radiology.

[42]  Alona Muzikansky,et al.  First-line gefitinib in patients with advanced non-small-cell lung cancer harboring somatic EGFR mutations. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[43]  J. Crowley,et al.  The IASLC Lung Cancer Staging Project: Proposals for the Revisions of the T Descriptors in the Forthcoming Eighth Edition of the TNM Classification for Lung Cancer , 2007, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[44]  B. Nan,et al.  CT-guided thoracic core biopsies: value of a negative result , 2006, Cancer imaging : the official publication of the International Cancer Imaging Society.

[45]  S. Gabriel,et al.  EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy , 2004, Science.

[46]  Patricia L. Harris,et al.  Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. , 2004, The New England journal of medicine.

[47]  William Pao,et al.  Bronchioloalveolar pathologic subtype and smoking history predict sensitivity to gefitinib in advanced non-small-cell lung cancer. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[48]  David Cella,et al.  Efficacy of gefitinib, an inhibitor of the epidermal growth factor receptor tyrosine kinase, in symptomatic patients with non-small cell lung cancer: a randomized trial. , 2003, JAMA.

[49]  Chris H. Q. Ding,et al.  Minimum redundancy feature selection from microarray gene expression data , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.

[50]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[51]  R. Tibshirani,et al.  An Introduction to the Bootstrap , 1995 .

[52]  E. Dmitrovsky,et al.  Differential expression of the epidermal growth factor receptor and its ligands in primary non-small cell lung cancers and adjacent benign lung. , 1993, Cancer research.

[53]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[54]  A. Jemal,et al.  Cancer statistics, 2016 , 2016, CA: a cancer journal for clinicians.

[55]  Massimo Bellomi,et al.  CT Radiogenomic Characterization of EGFR, K-RAS, and ALK Mutations in Non-Small Cell Lung Cancer , 2015, European Radiology.

[56]  V. Goh,et al.  Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. , 2013, Radiology.

[57]  Hsin-Chieh Yeh,et al.  Effect of the 2011 vs 2003 duty hour regulation-compliant models on sleep duration, trainee education, and continuity of patient care among internal medicine house staff: a randomized trial. , 2013, JAMA internal medicine.

[58]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[59]  K. Ahrar,et al.  CT-guided percutaneous needle biopsy of intrathoracic lesions by using the transsternal approach: experience in 37 patients. , 2002, Radiology.

[60]  N. Dubrawsky.  Cancer statistics , 1989, CA: a cancer journal for clinicians.

[61]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .