| 董佳寒,周霖,王肖辉.基于肝细胞癌超声表现回归模型列线图预测微血管侵犯[J].中国医学影像技术,2025,41(10):1682~1686 |
| 基于肝细胞癌超声表现回归模型列线图预测微血管侵犯 |
| Nomogram of hepatocellular carcinoma ultrasonic feature regression model for predicting microvascular invasion |
| 投稿时间：2024-12-26 修订日期：2025-06-11 |
| DOI：10.13929/j.issn.1003-3289.2025.10.016 |
| 中文关键词: 癌,肝细胞 超声检查 列线图 预测 微血管侵犯 |
| 英文关键词:carcinoma, hepatocellular ultrasonography nomograms forecasting microvascular invasion |
| 基金项目:河南省医学科技攻关计划项目(SBGJ202402061)。 |
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| 中文摘要: |
| 目的 观察肝细胞癌(HCC)超声表现回归模型列线图预测微血管侵犯(MVI)的价值。方法 回顾性收集经病理确诊的400例HCC共432个病灶,按7 ∶ 3比例分为训练集与验证集:训练集280例302个病灶中,MVI(+)160例(172个病灶)、MVI(－)120例(130个病灶);验证集120例130个病灶中,MVI(+)70例(76个病灶)、MVI(－)50例(54个病灶)。采用单因素及多因素logistic回归分析HCC超声表现,筛选HCC MVI的独立预测因素,以之建立回归模型并构建列线图;评估列线图预测HCC MVI的效能、校准度及临床价值。结果 HCC最大径(OR=2.564)、边缘规则与否(OR=0.412)、内部回声均匀与否(OR=1.875)、有无包膜(OR=0.305),以及Adler血流分级(OR=3.502)均为HCC MVI的独立预测因素(P均＜0.05)。列线图在训练集的敏感度为84.88%、特异度为81.05%、准确率为83.11%、曲线下面积(AUC)为0.950,在验证集分别为78.95%、77.78%、78.46%及0.910。校准曲线及决策曲线分析提示列线图在训练集和验证集的校准度均良好、临床净获益均较高。结论 超声特征回归模型列线图可有效预测HCC MVI。 |
| 英文摘要: |
| Objective To explore the value of nomogram of hepatocellular carcinoma (HCC) ultrasonic feature regression model for predicting microvascular invasion (MVI). Methods A total of 400 HCC patients (432 lesions) confirmed by pathology were retrospectively collected and divided into training set and validation set at the ratio of 7 ∶ 3. There were 280 cases (302 lesions) in training set, including 160 cases (172 lesions) of MVI (+) and 120 cases (130 lesions) of MVI (－), while 120 cases (130 lesions) in validation set, including 70 cases (76 lesions) of MVI (+) and 50 cases (54 lesions) of MVI (－). Univariate and multivariate logistic regression analyses were performed to analyze ultrasonic manifestations of HCC. The independent predictors of MVI of HCC were screened out, and a regression model and nomogram were established, and the efficacy, calibration and clinical value of the nomogram for predicting MVI of HCC were evaluated. Results The maximum diameter of HCC (OR=2.564), margin regular or not (OR=0.412), internal echo uniform or not (OR=1.875), the presence or absence of capsule (OR=0.305) and Adler blood flow grade (OR=3.502) were all independent predictors of MVI of HCC (all P＜0.05). The sensitivity, specificity, accuracy and the area under the curve (AUC) of the nomogram for predicting MVI of HCC in training set was 84.88%, 81.05%, 83.11% and 0.950, respectively, while in validation set was 78.95%, 77.78%, 78.46% and 0.910, respectively. Calibration curve and decision curve analysis indicated that this nomogram had good calibration and high clinical net benefit in both training and validation sets. Conclusion Nomogram of ultrasound feature regression model could be used to effectively predict MVI of HCC. |
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