Automatic Staging and Subtype Determination for Non-Small Cell Lung Carcinoma Using PET Image Texture Analysis
 K. Abe, S. Baba, K. Kaneko, Isoda T, Yabuuchi H, Sasaki M, et al. “Diagnostic and prognostic values of FDG-PET in patients with non-small cell lung cancer,” Clin Imag.,vol. 33, pp. 90-95, 2009.
 Berghmans T, Dusart M, Paesmans M, Hossein-Foucher C, Buvat I, Castaigne C, et al. “Primary tumor standardized uptake value (SUVmax) measured on fluorodeoxyglucose positron emission tomography (FDG-PET) is of prognostic value for survival in non-small cell lung cancer (NSCLC): a systematic review and meta-analysis (MA),” by the European Lung Cancer Working Party for the IASLC Lung Cancer Staging Project. J Thorac Oncol., vol. 3, pp. 6-12, 2008.
 A. Pugachev, S. Ruan, S. Carlin, S.M. Larson, J. Campa, C.C. Ling, et al. “Dependence of FDG uptake on tumor microenvironment,” Int J Radiat Oncol., vol. 62, pp. 545-553, 2005.
 J.B. MacQueen, “Some Methods for classification and Analysis of Multivariate Observations,” Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press. pp. 281–297. MR 0214227. Zbl 0214.46201, 1967.
 S. Selvarajah and S. Kodituwakku, “Analysis and comparison of texture features for content based image retrieval,” Int J Latest Trends Computing, vol. 2(1), pp. 108-113, 2011.
 R.M. Haralic, K. Shanmugan, I.H. Dinstein, “Textural features for image classification,” IEEE Trans Syst Man Cybern Syst., vol. SMC-3(6), pp. 610-621, 1973.
 M.M. Galloway, “Texture analysis using gray level run lengths,” Comp Vision Graph., vol. 4, pp. 172-179, 1975.
 K.I. Laws, “Textured image segmentation” Ph.D. dissertation, University of Southern California, Los Angeles, CA, 1980.
 N.S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” The American Statistician, vol. 46 (3), pp. 175–185, 1992.
 C. Cortes and V. Vapnik, “Support-vector networks,” Mach Learn., vol. 20 , pp. 273-297, 1995.
 A.W. Whitney, “A direct method of nonparametric measurement selection,” IEEE Trans Comput. vol. 100, pp. 1100-1103, 1971.
 W. Vach, P.F. Høilund-Carlsen, O. Gerke and W.A. Weber, “Generating evidence for clinical benefit of PET/CT in diagnosing cancer patients,” J Nucl Med., vol. 52, pp. 77-85, 2011.
 G. Castellano, L. Bonilha, L. Li and F. Cendes, “Texture analysis of medical images,” Clin Radiol., vol. 59, pp. 1061-1069, 2004.
 K. Holli, A-L. Lääperi, L. Harrison, T. Luukkaala, T. Toivonen, P. Ryymin, et al., “Characterization of breast cancer types by texture analysis of magnetic resonance images,” Acad Radiol. vol. 17, pp. 135-141, 2010.
 F. Davnall, C.S. Yip, G. Ljungqvist, M. Selmi, F. Ng, B. Sanghera, et al., “Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?” Insights Imaging,vol. 3, pp. 573-589, 2012.
 A. Ba-Ssalamah, D. Muin, R. Schernthaner, C. Kulinna-Cosentini, N. Bastati, J. Stift, et al., “Texture-based classification of different gastric tumors at contrast-enhanced CT,” Eur J Radiol., vol. 82, pp. 537-543, 2013.
 G.J. Cook, C. Yip, M. Siddique, V. Goh, S. Chicklore, A. Roy, et al., “Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy?” J Nucl Med., vol. 54, pp. 19-26, 2013.
 F. Orlhac, M. Soussan, J-A. Maisonobe, C.A. Garcia, B. Vanderlinden, I. Buvat, “Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis,” J Nucl Med., vol. 55, pp. 414-422, 2014.
 S. Ha, H. Choi, G.J. Cheon, K.W. Kang, J-K. Chung, E.E. Kim, et al., “Autoclustering of non-small cell lung carcinoma subtypes on 18F-FDG PET using texture analysis: a preliminary result.” Nucl Med Mol Imaging., vol. 48, pp. 278-286, 2014.