Estimation of 18F-FDG PET Image Texture Features for Metastasis Prediction in Non-Small Cell Lung Cancer Using Epithelial Mesenchymal Transition-Related Genes

Purpose: The aim of this study was to estimate a metastasis prediction image factor in non-small cell lung cancer by correlation next generation sequence gene expression level and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography image features. Methods: : RNA-sequencing data and 18 F-FDG PET images of 63 patients with NSCLC (29 metastasis and 34 non-metastasis) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups were related metastasis. Module was selected with high module significance. Genes selection was performed by gene function related metastasis and high AUC (AUC > 0.6). A total of 47 image features were extracted from PET images as radiomics. The relationship of Gene expression and image features were calculated by using a hypergeometric distribution test with the Pearson correlation method. Metastasis prediction model was validated by random forest algorithm using image texture features related gene expression. Results: : 36 modules were identified by gene expression pattern with WGCNA assay. The modules had highest module significance was selected assay. 7 genes from selected module were identified to involve in the epithelial mesenchymal transition pathway that have important role in the cancer metastasis and had high AUC. Also, expression of these genes was related to quantitative of image feature (GLCM_contrast, -log10 P-value: 2.45~3.89). The AUC value (accuracy: 0.856 ± 0.06, AUC: 0.868 ± 0.05) was shown from the EMT-related gene and GLCM_contrast model and AUC value (accuracy: 0.842 ± 0.06, AUC: 0.838 ± 0.09) was shown from GLCM_contrast image texture model. Conclusion: GLCM_contrast image texture feature shows relationship with EMT related gene expression. We developed a model for predicting metastasis of non-small cell lung cancer using 18 F-FDG PET image feature and evaluated its accuracy.