Abstract：
OBJECTIVE: To develop a nomogram using clinical features and the MRI parameters for preoperatively predicting the expression of Ki-67 in patients with hepatocellular carcinoma (HCC).
METHODS: One hundred and forty patients (training cohorts: n = 108; validation cohorts: n = 32) with confirmed HCC were investigated. Mann-Whitney U test, independent sample t-test, and chi-squared test were used to analyze the continuous and categorical variables. Univariate and multivariate logistic regression analyses were performed to examine the clinical variables and parameters from MRI associated with Ki-67 expression. As a result, a nomogram was developed based on these associations in patients with HCC. The performance of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curves.
RESULTS: In the training set, multivariable logistic regression analysis revealed that lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3) levels, protein induced by vitamin K absence or antagonist-II (PIVKA-II) levels, and tumor shape were independent predictors for Ki-67 expression (p < 0.05). These three variables and the apparent diffusion coefficient (ADC) value were used to establish a nomogram, while the ADC value was found to be a marginal significant predictor. The model demonstrated a strong ability to discriminate Ki-67 expression in both the training and validation cohorts (AUC = 0.862, 0.877).
CONCLUSIONS: A non-invasive preoperative prediction method, which incorporates MRI variables and clinical features was developed, and showed effectiveness in evaluating Ki-67 expression in HCC patients.
METHODS: One hundred and forty patients (training cohorts: n = 108; validation cohorts: n = 32) with confirmed HCC were investigated. Mann-Whitney U test, independent sample t-test, and chi-squared test were used to analyze the continuous and categorical variables. Univariate and multivariate logistic regression analyses were performed to examine the clinical variables and parameters from MRI associated with Ki-67 expression. As a result, a nomogram was developed based on these associations in patients with HCC. The performance of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curves.
RESULTS: In the training set, multivariable logistic regression analysis revealed that lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3) levels, protein induced by vitamin K absence or antagonist-II (PIVKA-II) levels, and tumor shape were independent predictors for Ki-67 expression (p < 0.05). These three variables and the apparent diffusion coefficient (ADC) value were used to establish a nomogram, while the ADC value was found to be a marginal significant predictor. The model demonstrated a strong ability to discriminate Ki-67 expression in both the training and validation cohorts (AUC = 0.862, 0.877).
CONCLUSIONS: A non-invasive preoperative prediction method, which incorporates MRI variables and clinical features was developed, and showed effectiveness in evaluating Ki-67 expression in HCC patients.
摘要：
目的：使用临床特征和MRI参数建立列线图，以预测肝细胞癌（HCC）患者术前Ki-67的表达。
方法：调查了一百四十名确诊HCC患者（培训队列：n=108；验证队列：n=32）。Mann-WhitneyU测试,独立样本t检验，采用卡方检验对连续变量和分类变量进行分析。进行了单变量和多变量逻辑回归分析，以检查与Ki-67表达相关的MRI的临床变量和参数。因此,根据HCC患者的这些关联,得出列线图.使用接收器工作特征曲线下面积(AUC)和校准曲线评价列线图的性能。
结果：在训练集中，多因素logistic回归分析显示,头颅晶状体凝集素-甲胎蛋白反应组分(AFP-L3)水平,维生素K缺失或拮抗剂II（PIVKA-II）水平诱导的蛋白质，和肿瘤形态是Ki-67表达的独立预测因子(p<0.05)。这三个变量和表观扩散系数（ADC）值用于建立列线图，而ADC值被发现是一个边缘显著的预测因子。该模型显示出在训练和验证队列中辨别Ki-67表达的强能力(AUC=0.862,0.877)。
结论：一种无创的术前预测方法，结合MRI变量和临床特征的开发，并显示在评估肝癌患者Ki-67表达的有效性。
方法：调查了一百四十名确诊HCC患者（培训队列：n=108；验证队列：n=32）。Mann-WhitneyU测试,独立样本t检验，采用卡方检验对连续变量和分类变量进行分析。进行了单变量和多变量逻辑回归分析，以检查与Ki-67表达相关的MRI的临床变量和参数。因此,根据HCC患者的这些关联,得出列线图.使用接收器工作特征曲线下面积(AUC)和校准曲线评价列线图的性能。
结果：在训练集中，多因素logistic回归分析显示,头颅晶状体凝集素-甲胎蛋白反应组分(AFP-L3)水平,维生素K缺失或拮抗剂II（PIVKA-II）水平诱导的蛋白质，和肿瘤形态是Ki-67表达的独立预测因子(p<0.05)。这三个变量和表观扩散系数（ADC）值用于建立列线图，而ADC值被发现是一个边缘显著的预测因子。该模型显示出在训练和验证队列中辨别Ki-67表达的强能力(AUC=0.862,0.877)。
结论：一种无创的术前预测方法，结合MRI变量和临床特征的开发，并显示在评估肝癌患者Ki-67表达的有效性。
