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中华肝脏外科手术学电子杂志

中华肝脏外科手术学电子杂志 ›› 2019, Vol. 08 ›› Issue (02) : 154 -158. doi: 10.3877/cma.j.issn.2095-3232.2019.02.016

临床研究

18F-FDG PET-CT影像组学鉴别中低分化肝细胞癌和肝内胆管细胞癌
周子东1, 查悦明1, 黄文山1, 杨敏1, 张桂雄1, 许杰华1,†()   
  1. 1. 510630　广州，中山大学附属第三医院核医学科
  • 收稿日期:2018-12-20 出版日期:2019-04-10
  • 通信作者: 许杰华
  • 基金资助:
    国家自然科学基金(81101866); 广东省自然科学基金(2018A030313200)

Differential diagnosis of moderately- or poorly-differentiated hepatocellular carcinoma from intrahepatic cholangiocarcinoma based on 18F-FDG PET-CT radiomics features

Zidong Zhou1, Yueming Zha1, Wenshan Huang1, Min Yang1, Guixiong Zhang1, Jiehua Xu1,†()   

  1. 1. Department of Nuclear Medicine, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
  • Received:2018-12-20 Published:2019-04-10
  • Corresponding author: Jiehua Xu
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引用本文：

周子东, 查悦明, 黄文山, 杨敏, 张桂雄, 许杰华. 18F-FDG PET-CT影像组学鉴别中低分化肝细胞癌和肝内胆管细胞癌[J/OL]. 中华肝脏外科手术学电子杂志, 2019, 08(02): 154-158.

Zidong Zhou, Yueming Zha, Wenshan Huang, Min Yang, Guixiong Zhang, Jiehua Xu. Differential diagnosis of moderately- or poorly-differentiated hepatocellular carcinoma from intrahepatic cholangiocarcinoma based on 18F-FDG PET-CT radiomics features[J/OL]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2019, 08(02): 154-158.

目的

探讨18F-氟脱氧葡萄糖（18F-FDG）PET-CT影像组学方法鉴别中低分化肝细胞癌（HCC）和肝内胆管细胞癌（ICC）的可行性。

方法

本前瞻性研究对象为2015年6月至2018年4月在中山大学附属第三医院行18F-FDG PET-CT检查的36例原发性肝癌患者。患者签署知情同意书，符合医学伦理学规定。其中男31例，女5例；年龄21~74岁，中位年龄52岁。中低分化HCC 26例，ICC 10例。利用3D Slicer软件在18F-FDG PET-CT图像上勾画病灶感兴趣区体积，提取每个病灶的影像组学特征。105个影像组学特征经LASSO回归模型进行筛选和优化，构建影像组学标签诊断模型。采用受试者工作特征（ROC）曲线检验该模型在鉴别中低分化HCC和ICC中的诊断效能。

结果

每个病灶均得到105个PET-CT影像组学特征，筛选和优化后得到2个PET-CT影像组学特征Sphericity和ZoneVariance，纳入Logistic回归模型，得到影像组学标签诊断模型为logit（P）=-8.984+10.506×Sphericity+61.341×ZoneVariance。该模型的ROC曲线下面积为0.923，95%CI：0.84~1.00，敏感度为1.00，特异度为0.73。

结论

18F-FDG PET-CT影像组学能较好地鉴别中低分化HCC和ICC，具有较高的敏感度和特异度，诊断价值高。

关键词: 癌，肝细胞, 胆管上皮癌, 正电子发射断层显像术, 影像组学
Objective

To investigate the feasibility of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography with computed tomography (PET-CT) radiomics in the differential diagnosis of moderately- or poorly-differentiated hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC).

Methods

In this prospective study, 36 patients with primary liver cancer receiving 18F-FDG PET-CT in the Third Affiliated Hospital of Sun Yat-sen University from June 2015 to April 2018 were recruited. The informed consents of all patients were obtained and the local ethical committee approval was received. Among them, 31 patients were male and 5 female, aged 21-74 years with a median age of 52 years. 26 patients were diagnosed with moderately- or poorly-differentiated HCC and 10 of ICC. The volume of region of interests on the 18F-FDG PET-CT images was delineated by using 3D Slicer software. The radiomic features of each lesion were extracted. A total of 105 radiomic features were screened and optimized by LASSO regression model to construct a diagnostic model with radiomics signatures. The efficacy of this model in differential diagnosis from moderately- or poorly-differentiated HCC and ICC was evaluated by the receiver operating characteristic (ROC) curve.

Results

In total, 105 PET-CT radiomic features were obtained for each lesion. After screening and optimization, 2 PET-CT radiomic features, Sphericity and ZoneVariance, were sorted and included into the Logistic regression model. The diagnostic model with radiomics signatures was logit (P) =-8.984+10.506×Sphericity+61.341×ZoneVariance. The area under ROC curve of this model was 0.923, 95%CI: 0.84-1.00, with the sensitivity 1.00 and specificity 0.73.

Conclusions

18F-FDG PET-CT radiomics can be adopted to differentiate moderately- or poorly-differentiated HCC from ICC with a high sensitivity, specificity and diagnostic value.

Key words: Carcinoma, hepatocellular, Cholangiocarcinoma, Positron-emission tomography, Radiomics
图1 利用LASSO回归筛选影像组学参数过程 注：a为LASSO回归模型的特征系数收敛图。每一条曲线代表了每一个自变量系数的变化轨迹，随着参数log(λ)的增大，对模型的变量惩罚力度加大，从而使无关变量的系数趋向于0。下横轴为log(λ)，上横轴数值为对应的特征数量，纵轴为特征系数。b为LOOCV验证方法挑选出最佳λ值。纵轴是偏似然偏差。下横轴表示log(λ)值。上横轴数值表示对应的特征数量。本研究中取左边的第1条垂直虚线对应的最优参数取值，右边的垂直虚线在标准误差允许范围内
图2 PET-CT影像组学标签诊断模型预测结果的ROC曲线 注：AUC为ROC曲线下面积，PET-CT为正电子发射计算机断层显像，ROC为受试者工作特征
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