Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

• Published in: Journal of digital imaging Vol. 31; no. 2; pp. 210 - 223
• Main Authors: Karacavus, Seyhan, Yılmaz, Bülent, Tasdemir, Arzu, Kayaaltı, Ömer, Kaya, Eser, İçer, Semra, Ayyıldız, Oguzhan
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-04-2018; Springer Nature B.V
• Subjects: Automatic classification; Classification; Feature extraction; FLUORINE 18; Image processing; Imaging; Lung cancer; LUNGS; Medicine; Medicine & Public Health; Metabolism; NEOPLASMS; Non-small cell lung carcinoma; Nonuniformity; Parameters; PATIENTS; POSITRON COMPUTED TOMOGRAPHY; Positron emission; Positron emission tomography; RADIATION DOSE UNITS; Radiology; RADIOLOGY AND NUCLEAR MEDICINE; SI UNITS; Statistical analysis; Support vector machines; Texture; Tomography; Tumor heterogeneity; Ki-67; Texture analysis; Tumor histopathological characteristics; PET
• ISSN: 0897-1889; 1618-727X; 1618-727X
• DOI: 10.1007/s10278-017-9992-3

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.