Karacavus, SeyhanYilmaz, BulentTasdemir, ArzuKayaalti, OmerKaya, EserIcer, SemraAyyildiz, Oguzhan2025-09-252025-09-2520180897-18891618-727Xhttps://doi.org/10.1007/s10278-017-9992-3https://hdl.handle.net/20.500.12573/3414Kayaalti, Omer/0000-0002-1630-1241; Yilmaz, Bulent/0000-0003-2954-1217;We investigated the association between the textural features obtained from F-18-FDG images, metabolic parameters (SUVmax(,) SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.eninfo:eu-repo/semantics/openAccessTexture AnalysisPETTumor HeterogeneityTumor Histopathological CharacteristicsKI-67Article10.1007/s10278-017-9992-32-s2.0-85021973667