Seyhan Karaçavuş and Bülent Yılmaz and Ömer Kayaaltı and Semra İçer and Arzu Taşdemir and Oğuzhan Ayyıldız and Kübra Eset and Eser Kaya
Automatic Staging and Subtype Determination for NonSmall Cell Lung Carcinoma Using PET Image Texture Analysis
123 - 126
2016
10
2
International Journal of Biomedical and Biological Engineering
https://publications.waset.org/pdf/10006805
https://publications.waset.org/vol/110
World Academy of Science, Engineering and Technology
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., nonsmall 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 18FFDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., III, 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), graylevel cooccurrence matrix (GLCM), graylevel runlength 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 knearest neighbors (kNN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5 with kNN classifier (k3) 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 FDGPET 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.
Open Science Index 110, 2016