[1]周榴,董怡,夏威,等.基于超声影像组学的原发性肝细胞癌分级预测[J].中国医学物理学杂志,2020,37(1):59-64.[doi:DOI:10.3969/j.issn.1005-202X.2020.01.012]
　ZHOU Liu,DONG Yi,XIA Wei,et al.Prediction of grade of hepatocellular carcinoma by radiomics based on ultrasound[J].Chinese Journal of Medical Physics,2020,37(1):59-64.[doi:DOI:10.3969/j.issn.1005-202X.2020.01.012]
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基于超声影像组学的原发性肝细胞癌分级预测()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第1期
页码:
59-64
栏目:
医学影像物理
出版日期:
2020-01-25

文章信息/Info

Title:
Prediction of grade of hepatocellular carcinoma by radiomics based on ultrasound
文章编号:
1005-202X（2020）01-0059-06
作者:
周榴1; 董怡2; 夏威3; 赵星羽3; 张琪2; 王文平2; 高欣3; 杨军1
1.中国医学科学院北京协和医学院生物医学工程研究所， 天津 300192； 2.复旦大学附属中山医院超声科， 上海 200032； 3.中国科学院苏州生物医学工程技术研究所， 江苏 苏州 215163
Author(s):
ZHOU Liu1;  DONG Yi2;  XIA Wei3;  ZHAO Xingyu3;  ZHANG Qi2;  WANG Wenping2;  GAO Xin3;  YANG Jun1
1. Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin 300192, China; 2. Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200032, China; 3. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
关键词:
原发性肝细胞癌; 影像组学; 分化等级; 相关特征
Keywords:
Keywords: hepatocellular carcinoma;  radiomics;  differentiation grade;  related feature
分类号:
R318；R735.7
DOI:
DOI:10.3969/j.issn.1005-202X.2020.01.012
文献标志码:
A
摘要:
目的：针对原发性肝细胞癌（HCC）肿瘤分级预测难题，提出一种基于灰阶超声成像的影像组学预测模型。方法：首先，由超声医生对肿瘤区域进行手动分割，其次，采用影像组学方法对肿瘤区域提取形状、一阶统计、纹理特征，计算特征间Pearson相关系数剔除冗余特征，最后通过单变量分析筛选得到特征子集，采用LASSO构建HCC分级预测模型；利用留一法计算模型的受试者操作特性曲线下的面积（AUC）评估模型对HCC分级的预测能力。结果：利用43例经手术病理证实的HCC患者的灰阶超声图像构建HCC分级预测模型，所建模型由6个与分级高度相关的影像特征组成，模型具有较强的预测能力（AUC=0.76）。结论：基于灰阶超声成像的影像特征与HCC分级高度相关，所建影像组学模型能够较好地预测HCC分级。
Abstract:
Abstract: Objective To propose a radiomics model based on gray-scale ultrasound images for solving the problem of predicting the grade of hepatocellular carcinoma (HCC). Methods Firstly, the tumor areas were segmented by an ultrasound physician, and then various features of tumor areas, including shape, the first order statistical properties and texture features were extracted by radiomics. Pearson’s correlation coefficient was used to eliminate the redundant features. Finally, univariate analysis was used for obtaining the optimal feature subset, and LASSO for constructing a model for predicting the grade of HCC. The area under the receiver operating characteristic curve (AUC) of the model was calculated by leave-one-of-cross validation so as to evaluate the prediction ability of the model. Results The radiomics model for prediction of the grade of HCC was constructed using gray-scale ultrasound images of 43 cases of HCC confirmed by operation and pathology. The obtained model was composed of 6 image features which was highly correlated with grading, and the results showed that the proposed model had preferable predication performances (AUC=0.76). Conclusion The image features based on gray-scale ultrasound images are highly correlated with the grade of HCC. The established radiomics model can be used to better predict the grade of HCC.

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备注/Memo

备注/Memo:
【收稿日期】2019-08-12 【基金项目】国家自然科学基金（81871439）；江苏省重点研发计划（BE2017671） 【作者简介】周榴，硕士研究生，研究方向：基于组学大数据的肝癌精准诊断，E-mail: zhouliu_96@163.com 【通信作者】杨军，研究员，研究方向：医学超声工程、医疗仪器与技术、生理信息检测、信号处理，E-mail: Yangj3210@hotmail.com
更新日期/Last Update: 2020-01-14