Staging of cervical cancer based on tumor heterogeneity characterized by texture features on F-18-FDG PET images

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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, Zhe 1,2; Liang, Ying 3; Shen, Wei1,2 ; Yang, Feng 4; Dai, Ruwei1,2 ; Wu, Ning 3; Tian, Jie1,2
刊名	PHYSICS IN MEDICINE AND BIOLOGY
	2015-07-07
卷号	60 期号:13 页码:5123-5139
关键词	cervical cancer PET/CT images tumor segmentation texture analysis cancer staging
英文摘要	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.
WOS标题词	Science & Technology ; Technology ; Life Sciences & Biomedicine
类目[WOS]	Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
研究领域[WOS]	Engineering ; Radiology, Nuclear Medicine & Medical Imaging
关键词[WOS]	SUPPORT VECTOR MACHINES ; ACTIVE CONTOURS ; LUNG-CANCER ; QUANTIFICATION ; SEGMENTATION ; RADIOTHERAPY ; ALGORITHM ; VOLUMES ; CT
收录类别	SCI
语种	英语
WOS记录号	WOS:000356872000013
公开日期	2015-09-22
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/7902]
专题	自动化研究所_中国科学院分子影像重点实验室
作者单位	1.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
推荐引用方式 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.