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基于临床-影像组学列线图模型术前预测进展期胃癌神经侵犯的价值
The value of nomogram model based on clinical and radiomics features in preoperative prediction of perineural invasion of advanced gastric cancer

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黄钰迅 1,2,3,4   李瑞 1,2,3,4   张宝腾 5   牛猛 2,3,4   刘钊 2,3,4   郭顺林 2,3,4 *  
文摘 目的:探讨基于增强CT影像组学特征和临床独立危险因素构建的联合模型及其列线图在术前预测进展期胃癌(AGC)周围神经侵犯(PNI)中的价值。方法:回顾性分析171例AGC患者的CT图像和临床资料。将171例患者按7:3的比例随机分为训练组119例(PNI阳性83例,阴性36例)和验证组52例(PNI阳性37例,阴性15例)。依次使用Spearman相关性分析及绝对收缩与选择算子(LASSO)对增强CT静脉期图像上提取的组学特征进行降维和筛选,并建立影像组学标签(V-Radscore) 。使用单因素分析比较PNI阳性组与阴性组之间的V-Radscore和术前临床指标值,将差异具有统计学意义的指标纳入多因素logistic回归分析,得到PNI相关的独立危险因素,同时构建影像组学模型(V)、临床模型(C)和组合模型(V+C),并在训练组构建组合模型的列线图。采用受试者工作特征曲线(ROC)的曲线下面积(AUC)、敏感度、特异度和符合率来评价模型的诊断效能,使用校准曲线评价列线图模型在训练组和验证组中的拟合程度,使用决策曲线分析(DCA)来评价列线图的临床应用价值。结果:PNI的独立危险因素包括V-Radscore、CT报告的肿瘤部位、T分期和N分期(P均<0.05)。 PNI阳性组的V-Radscore高于阴性组(Z=5.536,P<0.001)。在验证组中,列线图模型预测PNI的AUC值为0.865,显著高于临床模型(AUC=0.786,χ~2=2.108,P=0.035)和影像组学模型(AUC= 0.681,χ~2=2.083,P=0.037),其预测PNI的敏感度、特异度和符合率分别为0.838、0.800和0.827。校准曲线显示列线图在训练组(χ~2=5.846,P=0.661>0.05)及验证组(χ~2=8.170,P=0.417>0.05)中的预测概率与实际概率的一致性良好。DCA显示模型具有良好的临床应用价值。结论:临床-影像组学列线图模型在AGC患者PNI的术前预测方面具有可行性,有望帮助临床医师优化术前决策。
其他语种文摘 Objective:To explore the value of nomogram model based on contrast-enhanced CT radiomics and clinical features in preoperative prediction of perineural invasion(PNI)of advanced gastric cancer(AGC).Methods:The CT images and clinical data of 171patients with AGC were retrospectively analyzed.All patients were randomly divided into 119patients(including 83patients with PNI and 36patients without PNI)in the training cohort,and 52patients(including 37patients with PNI and 15patients without PNI)in the testing cohort at a rate of 7:3.The spearman correlation analysis (SPM)and least absolute shrinkage and selection operator(LASSO)were used for dimension reduction and selection of the radiomics features extracted from contrast-enhanced CT venous images,and the selected features were used to calculate the Radscore of venous images(V-Radscore).Univariate analysis was used to compare V-Radscore and the preoperative clinical indicators between PNI positive and PNI negative groups,and the statistically significant indicators were incorporated into multivariate logistic regression to obtain the independent risk factors of PNI,the radiomics model(V),clinical model(C)and combined model(C+V)were built at the same time,and then a nomogram was developed to predict PNI in training group.The area under curve(AUC)of receiver operating characteristic (ROC),sensitivity,specificity and accuracy were used to evaluate the diagnostic efficiency of nomogram model.The calibration curves and decision curve analysis(DCA)were used to assess the calibration and clinical usefulness of nomogram model,respectively.Results:The independent risk factors of PNI included V-Radscore,CT-reported tumor site,T-stage,and N-stage(P<0.05),and the V-Radscore in PNI positive group was significantly higher than that in negative group (Z =5.536,P < 0.001).In the testing cohort,the AUC of nomogram model for predicting PNI was 0.865,which was significantly higher than that of the clinical model(AUC=0.786,χ~2=2.108,P=0.035)and the radiomics model(AUC=0.681,χ~2=2.083,P=0.037),with the sensitivity,specificity and accuracy were 0.838,0.800and 0.827,respectively.The calibration curves showed a good consistency between the predicted and the actual probability in training(χ~2=5.846,P=0.664>0.05)and testing cohort(χ~2= 8.170,P=0.417>0.05).DCA curve showed that the nomogram model had good cli-nical application value.Conclusion:It is feasible to preoperatively predict PNI in patients with AGC using clinic-radiomics nomogram model,which is expected to assist clinicians in optimizing pre-operative decisionmaking.
来源 放射学实践 ,2022,37(12):1548-1554 【核心库】
DOI 10.13609/j.cnki.1000-0313.2022.12.015
关键词 影像组学 ; 神经侵犯 ; 进展期胃癌 ; 列线图 ; 诊断模型 ; 体层摄影术,X线计算机
地址

1. 兰州大学第一临床医学院, 兰州, 730000  

2. 兰州大学第一医院放射科, 兰州, 730000  

3. 甘肃省智能影像医学工程研究中心, 甘肃省智能影像医学工程研究中心, 兰州, 730000  

4. 精准影像协同创新甘肃省国际科技合作基地, 精准影像协同创新甘肃省国际科技合作基地, 兰州, 730000  

5. 莆田市第一医院医学影像科, 福建, 351100

语种 中文
文献类型 综述型
ISSN 1000-0313
学科 肿瘤学;特种医学
基金 国家自然科学基金
文献收藏号 CSCD:7368212

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引证文献 2 篇

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