Staging of cervical cancer based on tumor heterogeneity characterized by texture features on 18F-FDG PET images. (17th June 2015)

• Record Type: Journal Article
• Authors: Mu, Wei; Chen, Zhe; Liang, Ying; Shen, Wei; Yang, Feng; Dai, Ruwei; Wu, Ning; Tian, Jie
• Is Part Of: Physics in medicine & biology. Volume 60:Number 13(2015:Jul.)
• Page Start: 5123
• Page End: 5139
• Publication Date: 2015-06-17
• Subjects: cervical cancer -- PET/CT images -- tumor segmentation -- texture analysis -- cancer staging; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/0031-9155/60/13/5123
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: 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 18 F-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.