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中华诊断学电子杂志 ›› 2024, Vol. 12 ›› Issue (01) : 1 -8. doi: 10.3877/cma.j.issn.2095-655X.2024.01.001

机器学习

基于冠状动脉CT血管成像的影像组学列线图鉴别诊断易损斑块的价值
王林源1, 熊鑫1, 杨坤1, 邓勇志1,()   
  1. 1. 030024 太原,山西医科大学附属心血管病医院,山西省心血管病医院(研究所),山西省心血管病临床医学研究中心心脏大血管外科
  • 收稿日期:2023-07-07 出版日期:2024-02-26
  • 通信作者: 邓勇志
  • 基金资助:
    山西省医学重点科研项目重大科技攻关专项(2021XM04)

The value of radiomics nomogram based on coronary CT angiography in differential diagnosis of vulnerable plaques

Linyuan Wang1, Xin Xiong1, Kun Yang1, Yongzhi Deng1,()   

  1. 1. Department of Cardiovascular Surgery, the Affiliated Cardiovascular Hospital of Shanxi Medical University, Shanxi Cardiovascular Hospital (Institute), Shanxi Cardiovascular Disease Clinical Medical Research Center, Taiyuan 030024, China
  • Received:2023-07-07 Published:2024-02-26
  • Corresponding author: Yongzhi Deng
引用本文:

王林源, 熊鑫, 杨坤, 邓勇志. 基于冠状动脉CT血管成像的影像组学列线图鉴别诊断易损斑块的价值[J/OL]. 中华诊断学电子杂志, 2024, 12(01): 1-8.

Linyuan Wang, Xin Xiong, Kun Yang, Yongzhi Deng. The value of radiomics nomogram based on coronary CT angiography in differential diagnosis of vulnerable plaques[J/OL]. Chinese Journal of Diagnostics(Electronic Edition), 2024, 12(01): 1-8.

目的

探讨基于冠状动脉CT血管成像(CCTA)的影像组学列线图鉴别诊断冠状动脉疾病(CAD)患者易损斑块的价值。

方法

回顾性分析2018年1月至2022年12月在山西医科大学附属心血管病医院接受CCTA检查的93例CAD患者资料,共95个非钙化斑块,其中包括43个易损斑块,52个稳定斑块。以8∶2的比例随机将数据划分为训练集(n=76)及测试集(n=19)。应用Mann-Whitney U检验、相关系数法和最小绝对收缩选择算子(LASSO)回归选择最优的影像组学特征,构建影像组学模型并计算影像组学评分(Radscore)。同时,根据临床资料建立临床模型。结合临床因素和Radscore,构建影像组学列线图。采用受试者工作特征(ROC)曲线、校准曲线及决策曲线分析评估模型的性能。

结果

最终筛选出13个具有非零系数的最优特征。结合Radscore和临床因素的列线图模型在训练集和测试集中的AUC分别为1.000,0.922,敏感度为1.000,1.000,特异度为1.000,0.800,准确度为1.000,0.895。校准曲线显示列线图预测结果与实际结果之间具有良好的一致性。决策曲线分析表明,列线图比临床模型更具有临床应用价值。

结论

结合临床因素和影像组学特征的影像组学列线图能够准确、客观地鉴别诊断易损斑块,具有良好的诊断性能,可用于指导临床决策。

Objective

To investigate the value of radiomics nomogram based on coronary CT angiography (CCTA) in differential diagnosis of vulnerable plaques in patients with coronary artery disease (CAD).

Methods

From January 2018 to December 2022, 93 CAD patients who had undergone CCTA scans at the Affiliated Cardiovascular Hospital of Shanxi Medical University with complete data were enrolled. There were 95 non-calcified plaques, including 43 vulnerable plaques and 52 stable plaques. In an 8: 2 ratio, the lesions were randomly separated into a training set (n=76) and a testing set (n=19). To choose radiomics features appropriate for a CCTA-image-based radiomics signature, the Mann-Whitney U test, the correlation coefficient approach, and least absolute shrinkage and selection operator (LASSO) were used, and a radiomics score (Radscore) was created. Using clinical data and CCTA findings, a clinical model was constructed. Subsequently, the separate clinical parameters and Radscore were integrated to create a radiomics nomogram. The receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis were used to evaluate the performance of the radiomics signature, clinical model, and nomogram.

Results

Finally, 13 best features with non-zero coefficients were selected. In the training and test sets, the AUC of the nomogram model combined with Radscore and clinical variables were 1.000 and 0.922, sensitivity were 1.000 and 1.000, specificity were 1.000 and 0.800, and accuracy were 1.000 and 0.895, respectively. The calibration curve demonstrated good agreement between expected and actual results. The decision curve analysis revealed that the nomogram had greater clinical application value compared to the clinical model.

Conclusion

Combined with clinical factors and imaging features, the radiomics nomogram can accurately and objectively identify vulnerable plaques, and has good diagnostic performance, which can be used to guide clinical decision-making.

图1 55岁男性冠状动脉疾病患者左冠状动脉前降支斑块勾画示意图注:a图为冠状动脉CT血管成像原始图像;b图为手动分割斑块(箭头所示)
表1 训练集和测试集CAD患者临床特征和CCTA征象分析结果
项目 训练集(n=76) 统计量 P 测试集(n=19) 统计量 P
稳定斑块(n=42) 易损斑块(n=34) 稳定斑块(n=10) 易损斑块(n=9)
年龄(岁,±s) 55.83±9.73 57.03±11.30 t=0.496 0.622 58.00±9.17 50.33±13.65 t=-1.452 0.165
男[例(%)] 27(64.29) 31(91.18) χ2=7.517 0.006 6(60.00) 7(77.78)   0.628*
BMI[kg/m2M(Q1Q3),±s] 24.92(22.77,26.31) 24.66(22.27,27.52) Z=0.136 0.892 24.27±3.60 26.29±4.38 t=-1.098 0.287
高血压病[例(%)] 23(54.76) 25(73.53) χ2=2.844 0.092 4(40.00) 5(55.56)   0.656*
糖尿病[例(%)] 6(14.29) 8(23.53) χ2=1.068 0.301 2(20.00) 3(33.33)   0.628*
高脂血症[例(%)] 26(61.90) 29(85.29) χ2=5.140 0.023 6(60.00) 7(77.78)   0.628*
吸烟[例(%)] 16(38.10) 15(44.12) χ2=0.282 0.595 4(40.00) 5(55.56)   0.656*
总胆固醇[mmol/L,M(Q1Q3),±s] 4.10(3.53,4.75) 4.03(3.41,4.79) Z=0.016 0.987 4.84±1.17 5.12±1.92 t=-0.387 0.704
总甘油三酯[mmol/L,M(Q1Q3)] 1.49(0.97,2.09) 1.53(0.99,2.09) Z=0.199 0.843 1.46(0.985,3.22) 1.96(1.64,3.28) Z=-1.061 0.288
HDL-C[mmol/L,M(Q1Q3),±s] 1.00(0.87,1.16) 0.97(0.74,1.13) Z=1.102 0.270 1.18±0.37 1.02±0.26 t=-1.081 0.295
LDL-C[mmol/L,M(Q1Q3),±s] 2.61(1.93,2.87) 2.37(1.87,2.83) Z=1.008 0.313 2.77±0.62 3.05±1.24 t=0.628 0.539
传统斑块特征                
长度[mm,M(Q1Q3),±s] 13.72(12.18,16.60) 14.83(12.03,17.72) Z=0.757 0.449 15.04±1.86 16.87±2.68 t=1.738 0.100
体积[mm3M(Q1Q3)] 68.60(54.30,88.19) 93.83(60.27,127.26) Z=2.659 0.008 61.66(33.06,80.61) 91.97(69.60,166.79) Z=-1.960 0.050
斑块位置[例(%)]       0.297*       0.025*
前降支 36(85.71) 25(73.53)     8(80.00) 5(55.56)    
右冠状动脉 6(14.29) 8(23.53)     0 4(44.44)    
回旋支 0 1(2.94)     2(20.00) 0    
高危斑块特征[例(%)]                
低密度斑块 23(54.76) 34(100.00) χ2=20.510 <0.001 4(40.00) 9(100.00)   0.011*
正性重构 6(14.29) 27(79.41) χ2=32.439 <0.001 0 5(55.56)   0.011*
"餐巾环"征 1(2.38) 14(41.18) χ2=17.850 <0.001 0 4(44.44)   0.033*
图2 LASSO回归分析通过10折交叉验证的CAD患者基于CCTA的影像组学特征筛选过程图注:a图为基于CCTA提取的1 906个影像组学特征进行统计分析后的P值结果,以进行特征的初步筛选;firstorder为一阶统计特征;gldm为灰度依赖矩阵特征;glszm为灰度区域大小矩阵特征;shape为形状特征;b图为随参数λ变化的均方误差;c图为随参数λ变化的特征系数分布;d图为最终筛选的13个最优特征及其系数。a~d图通过10折交叉验证,不断调整超参数λ,以获取最优特征组合。LASSO为最小绝对收缩选择算子;CAD为冠状动脉疾病;CCTA为冠状动脉CT血管成像
表2 不同影像组学模型在训练集和测试集中鉴别诊断易损斑块的效能
图3 不同影像组学模型在测试集中鉴别诊断易损斑块的ROC曲线注:ROC为受试者工作特征;AUC为ROC曲线下面积
表3 临床模型、影像组学模型和列线图模型鉴别诊断易损斑块的ROC曲线结果
图4 基于影像组学评分与临床因素建立的列线图
图5 列线图、影像组学模型及临床模型鉴别诊断易损斑块的ROC曲线注:a图为训练集;b图为测试集;ROC为受试者工作特征;AUC为ROC曲线下面积
图6 列线图、影像组学及临床模型鉴别诊断易损斑块的校准曲线注:a图为训练集;b图为测试集
图7 列线图、影像组学及临床模型鉴别诊断易损斑块的决策曲线注:a图为训练集;b图为测试集
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