Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?
| Autor: | Seyhan Karaçavuş, Omer Kayaalti, Bulent Yilmaz, Arzu Tasdemir, Semra Icer, Oguzhan Ayyildiz, Eser Kaya |
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| Přispěvatelé: | Acibadem University Dspace, AGÜ, Mühendislik Fakültesi, Elektrik & Elektronik Mühendisliği Bölümü |
| Rok vydání: | 2017 |
| Předmět: |
Male
Tumor heterogeneity Lung Neoplasms Proliferation index Run length matrix Article 030218 nuclear medicine & medical imaging 03 medical and health sciences Second order statistics 0302 clinical medicine Image texture Fluorodeoxyglucose F18 Carcinoma Non-Small-Cell Lung Image Interpretation Computer-Assisted Tumor stage Humans Radiology Nuclear Medicine and imaging Computer vision Lung Tumor histopathological characteristics Retrospective Studies Radiological and Ultrasound Technology business.industry Pattern recognition Middle Aged Computer Science Applications Support vector machine PET Texture analysis Positron-Emission Tomography 030220 oncology & carcinogenesis Ki-67 Female Artificial intelligence Radiopharmaceuticals business Classifier (UML) |
| Zdroj: | Journal of Digital Imaging. 31:210-223 |
| ISSN: | 1618-727X 0897-1889 |
| DOI: | 10.1007/s10278-017-9992-3 |
| Popis: | This study was funded by TUBITAK (The Scientific and Technological Research Council of Turkey) under Project No.: 113E188. 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. TUBITAK (The Scientific and Technological Research Council of Turkey) - 113E188 |
| Databáze: | OpenAIRE |
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