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Abstract：
BACKGROUND: To develop and validate a nomogram model based on Gd-EOB-DTPA enhanced MRI for differentiation between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) showing iso- or hyperintensity in the hepatobiliary phase (HBP).
METHODS: A total of 75 patients with 49 HCCs and 26 FNHs randomly divided into a training cohort (n = 52: 34 HCC; 18 FNH) and an internal validation cohort (n = 23: 15 HCC; 8 FNH). A total of 37 patients (n = 37: 25 HCC; 12 FNH) acted as an external test cohort. The clinical and imaging characteristics between HCC and FNH groups in the training cohort were compared. The statistically significant parameters were included into the FAE software, and a multivariate logistic regression classifier was used to identify independent predictors and establish a nomogram model. Receiver operating characteristic (ROC) curves were used to evaluate the prediction ability of the model, while the calibration and decision curves were used for model validation. Subanalysis was used to compare qualitative and quantitative characteristics of patients with chronic hepatitis and cirrhosis between the HCC and FNH groups.
RESULTS: In the training cohort, gender, age, enhancement rate in the arterial phase (AP), focal defects in uptake were significant predictors for HCC showing iso- or hyperintensity in the HBP. In the training cohort, area under the curve (AUC), sensitivity and specificity of the nomogram model were 0.989(95%CI: 0.967-1.000), 97.1% and 94.4%. In the internal validation cohort, the above three indicators were 0.917(95%CI: 0.782-1.000), 93.3% and 87.5%. In the external test cohort, the above three indicators were 0.960(95%CI: 0.905-1.000), 84.0% and 100.0%. The results of subanalysis showed that age was the independent predictor in the patients with chronic hepatitis and cirrhosis between HCC and FNH groups.
CONCLUSIONS: Gd-EOB-DTPA enhanced MRI nomogram model may be useful for discriminating HCC and FNH showing iso- or hyperintensity in the HBP before surgery.
METHODS: A total of 75 patients with 49 HCCs and 26 FNHs randomly divided into a training cohort (n = 52: 34 HCC; 18 FNH) and an internal validation cohort (n = 23: 15 HCC; 8 FNH). A total of 37 patients (n = 37: 25 HCC; 12 FNH) acted as an external test cohort. The clinical and imaging characteristics between HCC and FNH groups in the training cohort were compared. The statistically significant parameters were included into the FAE software, and a multivariate logistic regression classifier was used to identify independent predictors and establish a nomogram model. Receiver operating characteristic (ROC) curves were used to evaluate the prediction ability of the model, while the calibration and decision curves were used for model validation. Subanalysis was used to compare qualitative and quantitative characteristics of patients with chronic hepatitis and cirrhosis between the HCC and FNH groups.
RESULTS: In the training cohort, gender, age, enhancement rate in the arterial phase (AP), focal defects in uptake were significant predictors for HCC showing iso- or hyperintensity in the HBP. In the training cohort, area under the curve (AUC), sensitivity and specificity of the nomogram model were 0.989(95%CI: 0.967-1.000), 97.1% and 94.4%. In the internal validation cohort, the above three indicators were 0.917(95%CI: 0.782-1.000), 93.3% and 87.5%. In the external test cohort, the above three indicators were 0.960(95%CI: 0.905-1.000), 84.0% and 100.0%. The results of subanalysis showed that age was the independent predictor in the patients with chronic hepatitis and cirrhosis between HCC and FNH groups.
CONCLUSIONS: Gd-EOB-DTPA enhanced MRI nomogram model may be useful for discriminating HCC and FNH showing iso- or hyperintensity in the HBP before surgery.
摘要：
背景：开发和验证基于Gd-EOB-DTPA增强MRI的列线图模型，用于区分肝细胞癌（HCC）和局灶性结节增生（FNH），在肝胆阶段（HBP）显示等强度或高强度。
方法：共有75例49例HCC和26例FNHs患者随机分为一个训练组（n=52:34HCC;18FNH）和一个内部验证组（n=23:15HCC;8FNH）。共有37名患者（n=37：25HCC;12FNH）作为外部测试队列。比较了训练队列中HCC和FNH组之间的临床和影像学特征。有统计学意义的参数被纳入FAE软件，并使用多元逻辑回归分类器识别独立预测因子并建立列线图模型。采用受试者工作特征(ROC)曲线评价模型的预测能力,而校准曲线和决策曲线用于模型验证。亚分析用于比较HCC和FNH组之间慢性肝炎和肝硬化患者的定性和定量特征。
结果：在培训队列中，性别,年龄,动脉期增强率（AP），局灶性摄取缺陷是HBP显示等强度或高强度的HCC的重要预测因子.在训练组中，曲线下面积（AUC），列线图模型的敏感性和特异性分别为0.989(95CI:0.967-1.000),97.1%和94.4%。在内部验证队列中，以上三项指标为0.917(95CI:0.782-1.000),93.3%和87.5%。在外部测试队列中，以上三项指标为0.960(95CI:0.905-1.000),84.0%和100.0%。亚分析结果表明，年龄是HCC和FNH组之间慢性肝炎和肝硬化患者的独立预测因素。
结论：Gd-EOB-DTPA增强MRI列线图模型可用于区分手术前HBP显示等强度或高强度的HCC和FNH。
方法：共有75例49例HCC和26例FNHs患者随机分为一个训练组（n=52:34HCC;18FNH）和一个内部验证组（n=23:15HCC;8FNH）。共有37名患者（n=37：25HCC;12FNH）作为外部测试队列。比较了训练队列中HCC和FNH组之间的临床和影像学特征。有统计学意义的参数被纳入FAE软件，并使用多元逻辑回归分类器识别独立预测因子并建立列线图模型。采用受试者工作特征(ROC)曲线评价模型的预测能力,而校准曲线和决策曲线用于模型验证。亚分析用于比较HCC和FNH组之间慢性肝炎和肝硬化患者的定性和定量特征。
结果：在培训队列中，性别,年龄,动脉期增强率（AP），局灶性摄取缺陷是HBP显示等强度或高强度的HCC的重要预测因子.在训练组中，曲线下面积（AUC），列线图模型的敏感性和特异性分别为0.989(95CI:0.967-1.000),97.1%和94.4%。在内部验证队列中，以上三项指标为0.917(95CI:0.782-1.000),93.3%和87.5%。在外部测试队列中，以上三项指标为0.960(95CI:0.905-1.000),84.0%和100.0%。亚分析结果表明，年龄是HCC和FNH组之间慢性肝炎和肝硬化患者的独立预测因素。
结论：Gd-EOB-DTPA增强MRI列线图模型可用于区分手术前HBP显示等强度或高强度的HCC和FNH。
