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18F-FDG PET radiomics approaches: comparing and clustering features in cervical cancer

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Abstract

Objectives

The aims of our study were to find the textural features on 18F-FDG PET/CT which reflect the different histological architectures between cervical cancer subtypes and to make a visual assessment of the association between 18F-FDG PET textural features in cervical cancer.

Methods

Eighty-three cervical cancer patients [62 squamous cell carcinomas (SCCs) and 21 non-SCCs (NSCCs)] who had undergone pretreatment 18F-FDG PET/CT were enrolled. A texture analysis was performed on PET/CT images, from which 18 PET radiomics features were extracted including first-order features such as standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), second- and high-order textural features using SUV histogram, normalized gray-level co-occurrence matrix (NGLCM), and neighborhood gray-tone difference matrix, respectively. These features were compared between SCC and NSCC using a Bonferroni adjusted P value threshold of 0.0028 (0.05/18). To assess the association between PET features, a heat map analysis with hierarchical clustering, one of the radiomics approaches, was performed.

Results

Among 18 PET features, correlation, a second-order textural feature derived from NGLCM, was a stable parameter and it was the only feature which showed a robust trend toward significant difference between SCC and NSCC. Cervical SCC showed a higher correlation (0.70 ± 0.07) than NSCC (0.64 ± 0.07, P = 0.0030). The other PET features did not show any significant differences between SCC and NSCC. A higher correlation in SCC might reflect higher structural integrity and stronger spatial/linear relationship of cancer cells compared with NSCC. A heat map with a PET feature dendrogram clearly showed 5 distinct clusters, where correlation belonged to a cluster including MTV and TLG. However, the association between correlation and MTV/TLG was not strong. Correlation was a relatively independent PET feature in cervical cancer.

Conclusions

18F-FDG PET textural features might reflect the differences in histological architecture between cervical cancer subtypes. PET radiomics approaches reveal the association between PET features and will be useful for finding a single feature or a combination of features leading to precise diagnoses, potential prognostic models, and effective therapeutic strategies.

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Acknowledgements

The authors thank Dr. Yu-Hua Dean Fang, the original developer of the Chang-Gung Image Texture Analysis toolbox, and the staff of the Department of Radiology and Biological Imaging Research Center, University of Fukui, for their clinical and technical supports.

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Corresponding author

Correspondence to Tetsuya Tsujikawa.

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Funding

This study was partly funded by Grants-in-Aid for scientific research from the Japan Society for the Promotion of Science (15H04981, 16K10345, and 16K20181) and Takeda Science Foundation.

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

This study was approved by the Ethics Committee of the University of Fukui, Faculty of Medical Sciences.

Consent to participate

Formal consent was not required for this type of retrospective study.

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Tsujikawa, T., Rahman, T., Yamamoto, M. et al. 18F-FDG PET radiomics approaches: comparing and clustering features in cervical cancer. Ann Nucl Med 31, 678–685 (2017). https://doi.org/10.1007/s12149-017-1199-7

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  • DOI: https://doi.org/10.1007/s12149-017-1199-7

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