CASIA OpenIR  > 中国科学院分子影像重点实验室
Staging of cervical cancer based on tumor heterogeneity characterized by texture features on F-18-FDG PET images
Mu, Wei1,2; Chen, Zhe1,2; Liang, Ying3; Shen, Wei1,2; Yang, Feng4; Dai, Ruwei1,2; Wu, Ning3; Tian, Jie1,2
Source PublicationPHYSICS IN MEDICINE AND BIOLOGY
2015-07-07
Volume60Issue:13Pages:5123-5139
SubtypeArticle
AbstractThe 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.
KeywordCervical Cancer Pet/ct Images Tumor Segmentation Texture Analysis Cancer Staging
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
WOS KeywordSUPPORT VECTOR MACHINES ; ACTIVE CONTOURS ; LUNG-CANCER ; QUANTIFICATION ; SEGMENTATION ; RADIOTHERAPY ; ALGORITHM ; VOLUMES ; CT
Indexed BySCI
Language英语
WOS Research AreaEngineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectEngineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000356872000013
Citation statistics
Cited Times:24[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7902
Collection中国科学院分子影像重点实验室
Affiliation1.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
2.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Chinese Acad Med Sci, Canc Inst & Hosp, Beijing 100021, Peoples R China
4.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China
Recommended Citation
GB/T 7714
Mu, Wei,Chen, Zhe,Liang, Ying,et al. Staging of cervical cancer based on tumor heterogeneity characterized by texture features on F-18-FDG PET images[J]. PHYSICS IN MEDICINE AND BIOLOGY,2015,60(13):5123-5139.
APA Mu, Wei.,Chen, Zhe.,Liang, Ying.,Shen, Wei.,Yang, Feng.,...&Tian, Jie.(2015).Staging of cervical cancer based on tumor heterogeneity characterized by texture features on F-18-FDG PET images.PHYSICS IN MEDICINE AND BIOLOGY,60(13),5123-5139.
MLA Mu, Wei,et al."Staging of cervical cancer based on tumor heterogeneity characterized by texture features on F-18-FDG PET images".PHYSICS IN MEDICINE AND BIOLOGY 60.13(2015):5123-5139.
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