鲁梦恬,张学琴,张涛,瞿琦,闫祖仪,顾春燕,徐磊,姜吉锋.MRI特征评分模型预测增殖型肝细胞癌[J].中国医学影像技术,2024,40(6):874~879
MRI特征评分模型预测增殖型肝细胞癌
Scoring model of MRI features for predicting proliferative hepatocellular carcinoma
投稿时间:2023-12-28  修订日期:2024-02-25
DOI:10.13929/j.issn.1003-3289.2024.06.016
中文关键词:  癌,肝细胞  磁共振成像  肝脏影像报告和数据系统
英文关键词:carcinoma, hepatocellular  magnetic resonance imaging  liver imaging reporting and data system
基金项目:南通市科技计划项目(MS2023069)、南通市卫生健康委科研课题(MS2023071)。
作者单位E-mail
鲁梦恬 南通大学医学院, 江苏 南通 226006
南通市第三人民医院放射科, 江苏 南通 226006 
 
张学琴 南通市第三人民医院放射科, 江苏 南通 226006 13962981245@163.com 
张涛 南通市第三人民医院放射科, 江苏 南通 226006  
瞿琦 南通大学医学院, 江苏 南通 226006
南通市第三人民医院放射科, 江苏 南通 226006 
 
闫祖仪 南通大学医学院, 江苏 南通 226006
南通市第三人民医院放射科, 江苏 南通 226006 
 
顾春燕 南通市第三人民医院病理科, 江苏 南通 226006  
徐磊 南通市第三人民医院放射科, 江苏 南通 226006  
姜吉锋 南通市第三人民医院放射科, 江苏 南通 226006  
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中文摘要:
      目的 观察MRI特征评分模型预测增殖型肝细胞癌(HCC)的价值。方法 回顾性分析241例经病理证实HCC患者,包括90例增殖型HCC、151例非增殖型HCC;采用单因素及多因素logistic回归分析比较组间临床及基于2018版肝脏影像报告和数据系统评估的MRI表现,筛选增殖型HCC的独立预测因素,根据其权重赋分并构建评分模型。绘制受试者工作特征(ROC)曲线,计算曲线下面积(AUC),评估模型预测效能;根据评分最佳截断值将患者分为高、低增殖风险亚组,比较组间及亚组间无复发生存(RFS)率和早期RFS率。结果 MRI显示肿瘤晕状强化、动脉期环形高强化、瘤内血管、大量病灶实质低强化及边缘不规则均为增殖型HCC的独立预测因素(OR=3.287、2.362、4.542、2.997、2.379,P均<0.05),分别赋分7、5、9、7、5分,总分0~33分;以之建立的评分模型预测增殖型HCC的AUC为0.818。以9分为最佳截断值进行判断,高、低增殖风险分别为97例及144例。组间、亚组间RFS率及早期RFS率差异均有统计学意义(P均<0.05)。结论 MRI特征评分模型可有效预测增殖型HCC。
英文摘要:
      Objective To observe the value of the scoring model of MRI features for predicting proliferative hepatocellular carcinoma (HCC). Methods Data of 241 patients with pathologically confirmed HCC, including 90 cases of proliferative HCC and 151 cases of non-proliferative HCC were analyzed retrospectively. Univariate and multivariate logistic regression were used to compare the clinical and MRI findings evaluated according to liver imaging reporting and data system version 2018 between groups. The independent predictive factors of proliferative HCC were screened, and scores were assigned according to the weight, then a scoring model was constructed. Receiver operating characteristic (ROC) curve was drawn, and the area under the curves (AUC) were calculated to assess the predictive efficacy of this model. The patients were divided into high and low proliferation risk subgroups based on the optimal score thresholds. The recurrence free survival (RFS) rates and early RFS rates were compared between groups and subgroups. Results MRI showed tumor corona enhancement, arterial phase annular hyper-enhancement, intratumoral vessels, much focus parenchymal low enhancement and irregular tumor margins were all independent predictive factors for proliferative HCC (OR=3.287, 2.362, 4.542, 2.997, 2.379, all P<0.05), which were then were scored with 7, 5, 9, 7 and 5, respectively, with a total score of 0—33. AUC of the obtained scoring model for predicting proliferative HCC was 0.818. Taken 9 points as the optimal score thresholds, 97 cases were assigned into high proliferation subgroup and 144 into low proliferation risk subgroups). Significant differences of RFS rates and early RFS rates were found between groups and subgroups (all P<0.05). Conclusion MRI features scoring model could effectively predict proliferative HCC.
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