Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?
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 wit...
| Published in: | Journal of Digital Imaging Vol. 31; no. 2; pp. 210 - 224 |
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| Main Authors: | , , , , , , |
| Format: | diagnostic images pictorial review tables/charts Journal Article |
| Published: |
Springer Nature
Apr2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=128715859&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128715859 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2018 vid: 31 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 128715859 128715859 128715859 10.1007/s10278-017-9992-3 128715859 ppf: 210 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC? aug: au: Karacavus, Seyhan Yılmaz, Bülent Tasdemir, Arzu Kayaaltı, Ömer Kaya, Eser İçer, Semra Ayyıldız, Oguzhan affil: Department of Nuclear Medicine, Saglık Bilimleri University, Kayseri Training and Research Hospital, 38010 Kayseri, Turkey sug: subj: Image Processing, Computer Assisted Tomography, Emission-Computed Carcinoma, Non-Small-Cell Lung Neoplasms Radiography Genetics Neoplasm Staging Validity Programming Languages Fludeoxyglucose F 18 Tumor Markers, Biological Cell Proliferation Tomography, Emission-Computed Methods Data Analysis, Statistical Machine Learning ab: 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. pubtype: Academic Journal doctype: diagnostic images pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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