基于影像组学方法的原发性肝细胞癌微血管侵犯和肿瘤分化等级预测
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摘要
目的:本文基于影像组学方法,用常规灰阶超声(GS-US)图像对原发性肝细胞癌(HCC)的微血管侵犯(MVI)指标和肿瘤分化等级进行预测。方法:根据影像组学的基本流程,本研究通过四个步骤:图像分割、特征提取、特征筛选和分类判别,分别建立对经手术病理证实的HCC病人的两个指标的预测模型并进行回顾性预测。结果:结果表明,肝脏的二维图像和MVI以及分化等级之间存在相关性,通过留一法(LOOCV)使用支持向量机(SVM)进行预测,受试者操作特性曲线下的面积(ROC)分别达到0.76(MVI)、0.89(肿瘤分化)。结论:灰阶超声图像蕴含和MVI以及肿瘤分化相关的相关信息,对HCC的灰阶超声图像的影像组学研究有助于患者的术前诊断和预后预测。
Purpose: The purpose of this study is to predict micro vascular invasion(MVI) and differentiation grade of hepatocellular carcinoma(HCC) by gray scale ultrasound(GS-US) images based on a radiomics approach. Methods: According to the routine of radiomics, our study built two predicting models for MVI and differentiation grade of histopathologically proved HCC patients respectively by 4 steps: image segmentation, feature extraction, feature selection and classification. Results: The results showed that, there were correlative relationships between GS-US images of HCC patients and MVI or tumor differentiation grade. By SVM with LOOCV, models produced AUC of 0.76(MVI) and 0.89(tumor differentiation grade) respectively. Conclusion: GS-US images can reveal the information about MVI and tumor differentiation grade. The radiomics study of HCC might be helpful for clinical diagnosis and prognosis prediction.
Purpose: The purpose of this study is to predict micro vascular invasion(MVI) and differentiation grade of hepatocellular carcinoma(HCC) by gray scale ultrasound(GS-US) images based on a radiomics approach. Methods: According to the routine of radiomics, our study built two predicting models for MVI and differentiation grade of histopathologically proved HCC patients respectively by 4 steps: image segmentation, feature extraction, feature selection and classification. Results: The results showed that, there were correlative relationships between GS-US images of HCC patients and MVI or tumor differentiation grade. By SVM with LOOCV, models produced AUC of 0.76(MVI) and 0.89(tumor differentiation grade) respectively. Conclusion: GS-US images can reveal the information about MVI and tumor differentiation grade. The radiomics study of HCC might be helpful for clinical diagnosis and prognosis prediction.
引文
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[14]胡国祥,郑丽民.肝细胞性肝癌CT表现与肿瘤病理分化相关性研究.中国中西医结合消化杂志,2013,21:537-538.
[15]骆雁翎,熊文文,张迎春,等.不同分化程度肝癌患者的超声造影参数分析.医学临床研究,2017,34:381-383.
[2]Yang J D,Roberts L R.Hepatocellular carcinoma:a global view.Nature Reviews Gastroenterology&Hepatology,2010,7:448.
[3]Edmondson H A,Steiner P E.Primary carcinoma of the liver:a study of 100 cases among 48,900 necropsies.Cancer,1954,7:462.
[4]张梅,徐雪房,李艳明,等.CT联合血清指标对不同组织分化程度肝细胞肝癌患者T ACE术后的多元分析评价.滨州医学院学报,2014:327-330.
[5]中国抗癌协会肝癌专业委员会.原发性肝细胞肝癌规范化病理诊断指南(2015年版).解放军医学杂志,2015,21:833-839.
[6]金赟,李江涛.肝癌细胞侵犯微血管的临床相关因素及分子标志物的研究进展.临床肝胆病杂志,2013,29:550-553.
[7]刘臻玉,武丹,区锦玲,等.肝癌微血管侵犯的特点及相关临床指标.肝胆胰外科杂志,2017,29:107-111.
[8]Lambin P,Riosvelazquez E,Leijenaar R,et al.Radiomics:Extracting more information from medical images using advanced feature analysis.European Journal of Cancer,2012,48:441-446.
[9]Yu J,Shi Z,Lian Y,et al.Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma.European Radiology,2016,26:1-14.
[10]Aerts H J,Velazquez E R,Leijenaar R T,et al.Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.Nature Communications,2014,5:4006.
[11]Li Y,Namburi P,Yu Z,et al.Voxel selection in f MRI data analysis based on sparse representation.IEEE transactions on bio-medical engineering,2009,56:2439.
[12]Gouw A S,Balabaud C,Kusano H,et al.Markers for microvascular invasion in hepatocellular carcinoma:where do we stand?.Liver Transplantation,2011,17 Suppl 2(Supplement S2):S72.
[13]Chou C T,Chen R C,Lee C W,et al.Prediction of microvascular invasion of hepatocellular carcinoma by pre-operative CT imaging.British Journal of Radiology,2012,85:778-783.
[14]胡国祥,郑丽民.肝细胞性肝癌CT表现与肿瘤病理分化相关性研究.中国中西医结合消化杂志,2013,21:537-538.
[15]骆雁翎,熊文文,张迎春,等.不同分化程度肝癌患者的超声造影参数分析.医学临床研究,2017,34:381-383.