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

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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.

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Nuclear Medicine, Saglık Bilimleri University, Kayseri Training and Research Hospital, 38010, Kayseri, Turkey

    Seyhan Karacavus

  2. Department of Biomedical Engineering, Erciyes University, Engineering Faculty, Kayseri, Turkey

    Seyhan Karacavus & Semra İçer

  3. Department of Electrical and Electronics Engineering, Abdullah Gül University, Engineering Faculty, Kayseri, Turkey

    Bülent Yılmaz & Oguzhan Ayyıldız

  4. Department of Pathology, Saglik Bilimleri University, Kayseri Training and Research Hospital, Kayseri, Turkey

    Arzu Tasdemir

  5. Department of Computer Technologies, Erciyes University, Develi Hüseyin Şahin Vocational College, Kayseri, Turkey

    Ömer Kayaaltı

  6. Department of Nuclear Medicine, Acibadem University, School of Medicine, İstanbul, Turkey

    Eser Kaya

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  1. Seyhan Karacavus
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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

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Correspondence to Seyhan Karacavus.

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Karacavus, S., Yılmaz, B., Tasdemir, A. et al. Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?. J Digit Imaging 31, 210–223 (2018). https://doi.org/10.1007/s10278-017-9992-3

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