Commenced in January 2007
Paper Count: 30855
Automatic Staging and Subtype Determination for Non-Small Cell Lung Carcinoma Using PET Image Texture Analysis
Abstract:In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129860Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 499
 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.