Journal
PHYSICS IN MEDICINE AND BIOLOGY
Volume 60, Issue 13, Pages 5123-5139Publisher
IOP Publishing Ltd
DOI: 10.1088/0031-9155/60/13/5123
Keywords
cervical cancer; PET/CT images; tumor segmentation; texture analysis; cancer staging
Funding
- National Basic Research Program of China (973 Program) [2011CB707700]
- National Natural Science Foundation of China [81227901, 61231004]
- Chinese Academy of Sciences Fellowship for Young International Scientists [2013Y1GB0005]
- National High Technology Research and Development Program of China (863 Program) [2012AA021105]
- Guangdong Province-Chinese Academy of Sciences comprehensive strategic cooperation program [2010A090100032, 2012B090400039]
- NSFC-NIH Biomedical collaborative research program [81261120414]
- Beijing Natural Science Foundation [4132080]
- Fundamental Research Funds for the Central Universities [2013JBZ014]
- National Basic Research Program of China [61301002, 61302025]
- Chinese Academy of Sciences Visiting Professorship for Senior International Scientists [2010T2G36]
- Chinese Academy of Sciences Key deployment program [KGZD-EW-T03]
- National Science and Technology Supporting Plan [2012BAI15B08]
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The aim of the study is to assess the staging value of the tumor heterogeneity characterized by texture features and other commonly used semi-quantitative indices extracted from F-18-FDG PET images of cervical cancer (CC) patients. Forty-two patients suffering CC at different stages were enrolled in this study. Firstly, we proposed a new tumor segmentation method by combining the intensity and gradient field information in a level set framework. Secondly, fifty-four 3D texture features were studied besides of SUVs (SUVmax, SUVmean, SUVpeak) and metabolic tumor volume (MTV). Through correlation analysis, receiver-operating-characteristic (ROC) curves analysis, some independent indices showed statistically significant differences between the early stage (ES, stages I and II) and the advanced stage (AS, stages III and IV). Then the tumors represented by those independent indices could be automatically classified into ES and AS, and the most discriminative feature could be chosen. Finally, the robustness of the optimal index with respect to sampling schemes and the quality of the PET images were validated. Using the proposed segmentation method, the dice similarity coefficient and Hausdorff distance were 91.78 +/- 1.66% and 7.94 +/- 1.99 mm, respectively. According to the correlation analysis, all the fifty-eight indices could be divided into 20 groups. Six independent indices were selected for their highest areas under the ROC curves (AUROC), and showed significant differences between ES and AS P < 0.05). Through automatic classification with the support vector machine (SVM) Classifier, run percentage (RP) was the most discriminative index with the higher accuracy (88.10%) and larger AUROC (0.88). The Pearson correlation of RP under different sampling schemes is 0.9991 +/- 0.0011. RP is a highly stable feature and well correlated with tumor stage in CC, which suggests it could differentiate ES and AS with high accuracy.
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