Journal of Digital Imaging

, Volume 31, Issue 2, pp 210–223 | Cite as

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

  • Seyhan Karacavus
  • Bülent Yılmaz
  • Arzu Tasdemir
  • Ömer Kayaaltı
  • Eser Kaya
  • Semra İçer
  • Oguzhan Ayyıldız


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.


Texture analysis PET Tumor heterogeneity Tumor histopathological characteristics Ki-67 



This study was funded by TUBITAK (The Scientific and Technological Research Council of Turkey) under Project No.: 113E188.

Author Contributions

Contributing to conception and design: SK, BY, AT, OK

Acquiring data, or analyzing and interpreting data: SK, BY, OK, OA, EK, SI

Drafting the manuscript: SK, BY, OK, SI

Critically contributing to or revising the manuscript, or enhancing its intellectual content: SK, BY, EK, SI

Approving the final content of the manuscript: SK, BY, OK

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


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Copyright information

© Society for Imaging Informatics in Medicine 2017

Authors and Affiliations

  • Seyhan Karacavus
    • 1
    • 2
  • Bülent Yılmaz
    • 3
  • Arzu Tasdemir
    • 4
  • Ömer Kayaaltı
    • 5
  • Eser Kaya
    • 6
  • Semra İçer
    • 2
  • Oguzhan Ayyıldız
    • 3
  1. 1.Department of Nuclear MedicineSaglık Bilimleri University, Kayseri Training and Research HospitalKayseriTurkey
  2. 2.Department of Biomedical EngineeringErciyes University, Engineering FacultyKayseriTurkey
  3. 3.Department of Electrical and Electronics EngineeringAbdullah Gül University, Engineering FacultyKayseriTurkey
  4. 4.Department of PathologySaglik Bilimleri University, Kayseri Training and Research HospitalKayseriTurkey
  5. 5.Department of Computer TechnologiesErciyes University, Develi Hüseyin Şahin Vocational CollegeKayseriTurkey
  6. 6.Department of Nuclear MedicineAcibadem University, School of MedicineİstanbulTurkey

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