A biomarker basing on radiomics for the prediction of overall survival in non–small cell lung cancer patients

[1] Parmar C(2015)Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer Sci Rep 5 11044-1380

[2] Leijenaar RTH(2008)Lung Cancer New England Journal of Medicine 359 1367-148

[3] Grossmann P(2011)Survival by histologic subtype in stage IV nonsmall cell lung cancer based on data from the surveillance, epidemiology and end results program Clin Epidemiol 3 139-2625

[4] Rios Velazquez E(2016)How to use CT texture analysis for prognostication of non-small cell lung cancer Cancer Imaging 16 10-305

[5] Bussink J(2014)A clustering-based graph Laplacian framework for value function approximation in reinforcement learning IEEE Trans Cybern 44 2613-577

[6] Rietveld D(2012)Random search for hyper-parameter optimization J Mach Learn Res 13 281-1397

[7] Rietbergen MM(2016)Radiomics: images are more than pictures, they are data Radiology 278 563-10

[8] Haibe-Kains B(2013)Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability Acta Oncol 52 1391-842

[9] Lambin P(2015)High quality machine-robust image features: identification in nonsmall cell lung cancer computed tomography images BMC Health Serv Res 15 1-486

[10] Aerts HJWL(2014)Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review PLoS One 9 e110300-12

[1] Parmar C(2015)Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer Sci Rep 5 11044-1380

[2] Leijenaar RTH(2008)Lung Cancer New England Journal of Medicine 359 1367-148

[3] Grossmann P(2011)Survival by histologic subtype in stage IV nonsmall cell lung cancer based on data from the surveillance, epidemiology and end results program Clin Epidemiol 3 139-2625

[4] Rios Velazquez E(2016)How to use CT texture analysis for prognostication of non-small cell lung cancer Cancer Imaging 16 10-305

[5] Bussink J(2014)A clustering-based graph Laplacian framework for value function approximation in reinforcement learning IEEE Trans Cybern 44 2613-577

[6] Rietveld D(2012)Random search for hyper-parameter optimization J Mach Learn Res 13 281-1397

[7] Rietbergen MM(2016)Radiomics: images are more than pictures, they are data Radiology 278 563-10

[8] Haibe-Kains B(2013)Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability Acta Oncol 52 1391-842

[9] Lambin P(2015)High quality machine-robust image features: identification in nonsmall cell lung cancer computed tomography images BMC Health Serv Res 15 1-486

[10] Aerts HJWL(2014)Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review PLoS One 9 e110300-12