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Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?
- Source :
- Journal of Digital Imaging; Apr2018, Vol. 31 Issue 2, p210-223, 14p, 2 Color Photographs, 1 Diagram, 4 Charts, 1 Graph
- Publication Year :
- 2018
-
Abstract
- We investigated the association between the textural features obtained from 18F-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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08971889
- Volume :
- 31
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of Digital Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 128715859
- Full Text :
- https://doi.org/10.1007/s10278-017-9992-3