Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
School of Radiation Medicine and Protection, Soochow University, Suzhou, China.
BMC Med Imaging. 2024 Aug 12;24(1):211. doi: 10.1186/s12880-024-01382-6.
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).
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.
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.
Gd-EOB-DTPA enhanced MRI nomogram model may be useful for discriminating HCC and FNH showing iso- or hyperintensity in the HBP before surgery.
为了建立并验证一个基于钆塞酸二钠增强 MRI 肝胆期（HBP）等/高信号的肝细胞癌（HCC）和局灶性结节增生（FNH）鉴别诊断的列线图模型。
共纳入 75 例患者，其中 49 例 HCC 和 26 例 FNH，随机分为训练队列（n=52：34 例 HCC；18 例 FNH）和内部验证队列（n=23：15 例 HCC；8 例 FNH）。另外 37 例（n=37：25 例 HCC；12 例 FNH）为外部测试队列。比较训练队列中 HCC 和 FNH 组的临床和影像学特征。将统计学上有意义的参数输入到 FAE 软件中，然后使用多变量逻辑回归分类器识别独立预测因子并建立列线图模型。通过受试者工作特征（ROC）曲线评估模型的预测能力，同时通过校准和决策曲线进行模型验证。亚组分析比较慢性肝炎和肝硬化患者 HCC 和 FNH 组的定性和定量特征。
在训练队列中，性别、年龄、动脉期（AP）强化率、摄取的局灶性缺损是 HCC 在 HBP 等/高信号的显著预测因子。在训练队列中，列线图模型的曲线下面积（AUC）、敏感性和特异性分别为 0.989（95%CI：0.967-1.000）、97.1%和 94.4%。在内部验证队列中，上述三项指标分别为 0.917（95%CI：0.782-1.000）、93.3%和 87.5%。在外部测试队列中，上述三项指标分别为 0.960（95%CI：0.905-1.000）、84.0%和 100.0%。亚组分析结果显示，年龄是慢性肝炎和肝硬化患者 HCC 和 FNH 组的独立预测因子。
钆塞酸二钠增强 MRI 列线图模型可用于术前鉴别诊断 HBP 等/高信号的 HCC 和 FNH。
