DOI: 10.1007/s00259-017-3697-1Pages: 1721-1731

[18]Fluorodeoxyglucose Positron Emission Tomography for the Textural Features of Cervical Cancer Associated with Lymph Node Metastasis and Histological Type

1. Asia University, Department of Computer Science and Information Engineering

2. China Medical University Hospital, Department of Radiation Oncology

3. China Medical University, School of Medicine

4. Taipei Medical University, School of Medicine

5. China Medical University, Graduate Institute of Clinical Medical Science, School of Medicine, College of Medicine

6. China Medical University Hospital, Department of Nuclear Medicine and PET Center

7. China Medical University, Department of Biomedical Imaging and Radiological Science

8. Asia University, Department of Bioinformatics and Medical Engineering

Correspondence to:
Chia-Hung Kao
Tel: +886-4-2205-2121




In this study, we investigated the correlation between the lymph node (LN) status or histological types and textural features of cervical cancers on 18F-fluorodeoxyglucose positron emission tomography/computed tomography.


We retrospectively reviewed the imaging records of 170 patients with International Federation of Gynecology and Obstetrics stage IB–IVA cervical cancer. Four groups of textural features were studied in addition to the maximum standardized uptake value (SUVmax), metabolic tumor volume, and total lesion glycolysis (TLG). Moreover, we studied the associations between the indices and clinical parameters, including the LN status, clinical stage, and histology. Receiver operating characteristic curves were constructed to evaluate the optimal predictive performance among the various textural indices. Quantitative differences were determined using the Mann–Whitney U test. Multivariate logistic regression analysis was performed to determine the independent factors, among all the variables, for predicting LN metastasis.


Among all the significant indices related to pelvic LN metastasis, homogeneity derived from the gray-level co-occurrence matrix (GLCM) was the sole independent predictor. By combining SUVmax, the risk of pelvic LN metastasis can be scored accordingly. The TLGmean was the independent feature of positive para-aortic LNs. Quantitative differences between squamous and nonsquamous histology can be determined using short-zone emphasis (SZE) from the gray-level size zone matrix (GLSZM).


This study revealed that in patients with cervical cancer, pelvic or para-aortic LN metastases can be predicted by using textural feature of homogeneity from the GLCM and TLGmean, respectively. SZE from the GLSZM is the sole feature associated with quantitative differences between squamous and nonsquamous histology.

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  • Accepted: Mar 28, 2017
  • Online: Apr 14, 2017

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