Name
Title | ||
|---|---|---|
Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC? |
Abstract | ||
|---|---|---|
We investigated the association between the textural features obtained from (18)F-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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10278-017-9992-3 | J. Digital Imaging |
Keywords | Field | DocType |
Texture analysis,PET,Tumor heterogeneity,Tumor histopathological characteristics,Ki-67 | Proliferation index,Computer vision,Image texture,Computer science,First order,Support vector machine,Tumor stages,Artificial intelligence,Classifier (linguistics) | Journal |
Volume | Issue | ISSN |
31 | 2 | 1618-727X |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seyhan Karacavus | 1 | 0 | 0.34 |
| Bülent Yilmaz | 2 | 4 | 2.48 |
| Arzu Tasdemir | 3 | 0 | 0.34 |
| Ömer Kayaalti | 4 | 4 | 0.79 |
| Eser Kaya | 5 | 0 | 0.34 |
| Semra İçer | 6 | 43 | 5.45 |
| Oguzhan Ayyıldız | 7 | 0 | 0.34 |