Radiomic phenotype features predict pathological response in non-small cell lung cancer.

[1]  P. Gibbs,et al.  Minkowski functionals: An MRI texture analysis tool for determination of the aggressiveness of breast cancer , 2016, Journal of magnetic resonance imaging : JMRI.

[2]  Peter Gibbs,et al.  Pretreatment Prognostic Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Vascular, Texture, Shape, and Size Parameters Compared With Traditional Survival Indicators Obtained From Locally Advanced Breast Cancer Patients , 2016, Investigative radiology.

[3]  A. Rao,et al.  Texture Feature Ratios from Relative CBV Maps of Perfusion MRI Are Associated with Patient Survival in Glioblastoma , 2016, American Journal of Neuroradiology.

[4]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[5]  P. Lambin,et al.  Machine Learning methods for Quantitative Radiomic Biomarkers , 2015, Scientific Reports.

[6]  Shao Hui Huang,et al.  External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma , 2015, Acta oncologica.

[7]  Benjamin Haibe-Kains,et al.  Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer , 2015, Scientific Reports.

[8]  G. Kalemkerian.  Running in Place: The 20th Anniversary of the NCCN Small Cell Lung Cancer Guidelines Panel. , 2015, Journal of the National Comprehensive Cancer Network : JNCCN.

[9]  Alejandro Munoz del Rio,et al.  CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes , 2015, Abdominal Imaging.

[10]  P. Lambin,et al.  CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

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

[12]  P. Lambin,et al.  Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation , 2014, PloS one.

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

[14]  P. Lambin,et al.  Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability , 2013, Acta oncologica.

[15]  Roberto Maroldi,et al.  Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy , 2013, European Radiology.

[16]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[17]  Andre Dekker,et al.  A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[18]  A. Terahara,et al.  Pathological response and prognosis of stage III non‐small cell lung cancer patients treated with induction chemoradiation , 2012, Asia-Pacific journal of clinical oncology.

[19]  B. Milleron,et al.  Pathologic Complete Response to Preoperative Chemotherapy Predicts Cure in Early-Stage Non–Small-Cell Lung Cancer: Combined Analysis of Two IFCT Randomized Trials , 2012, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[20]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[21]  Markus S. Schröder,et al.  survcomp: an R/Bioconductor package for performance assessment and comparison of survival models , 2011, Bioinform..

[22]  B. Alexander,et al.  Tumor volume is a prognostic factor in non-small-cell lung cancer treated with chemoradiotherapy. , 2011, International journal of radiation oncology, biology, physics.

[23]  C. Compton,et al.  The American Joint Committee on Cancer: the 7th Edition of the AJCC Cancer Staging Manual and the Future of TNM , 2010, Annals of Surgical Oncology.

[24]  V. Rusch,et al.  Radiotherapy plus chemotherapy with or without surgical resection for stage III non-small-cell lung cancer: a phase III randomised controlled trial , 2009, The Lancet.

[25]  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.

[26]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[27]  Benjamin Haibe-Kains,et al.  A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? , 2008, Bioinform..

[28]  T. Gauler,et al.  Correlation of PET/CT Findings and Histopathology after Neoadjuvant Therapy in Non-Small Cell Lung Cancer , 2008, Oncology.

[29]  G. Giaccone,et al.  Randomized controlled trial of resection versus radiotherapy after induction chemotherapy in stage IIIA-N2 non-small-cell lung cancer. , 2007, Journal of the National Cancer Institute.

[30]  M. Socinski,et al.  Post-chemotherapy gross tumor volume is predictive of survival in patients with stage III non-small cell lung cancer treated with combined modality therapy. , 2006, Lung cancer.

[31]  R. Cerfolio,et al.  Repeat FDG-PET after neoadjuvant therapy is a predictor of pathologic response in patients with non-small cell lung cancer. , 2004, The Annals of thoracic surgery.

[32]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[33]  M Werner-Wasik,et al.  Assessment of lung cancer response after nonoperative therapy: tumor diameter, bidimensional product, and volume. A serial CT scan-based study. , 2001, International journal of radiation oncology, biology, physics.

[34]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[35]  M. Kris,et al.  Pathological response after neoadjuvant chemotherapy in resectable non-small-cell lung cancers: proposal for the use of major pathological response as a surrogate endpoint. , 2014, The Lancet. Oncology.

[36]  A. Jemal,et al.  Cancer statistics, 2014 , 2014, CA: a cancer journal for clinicians.

[37]  L. Schwartz,et al.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). , 2009, European journal of cancer.

[38]  R. Kikinis,et al.  3D Slicer , 2004, ISBI.

[39]  Jeffrey D Bradley,et al.  Gross tumor volume, critical prognostic factor in patients treated with three-dimensional conformal radiation therapy for non-small-cell lung carcinoma. , 2002, International journal of radiation oncology, biology, physics.

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