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Logistic回归和人工神经网络在鉴别诊断肺癌性胸腔积液中的应用研究

李锐成 千红维 范艳妮 赵佩佩 魏姗 景花荣

李锐成, 千红维, 范艳妮, 赵佩佩, 魏姗, 景花荣. Logistic回归和人工神经网络在鉴别诊断肺癌性胸腔积液中的应用研究[J]. 昆明医科大学学报, 2024, 45(10): 55-60. doi: 10.12259/j.issn.2095-610X.S20241009
引用本文: 李锐成, 千红维, 范艳妮, 赵佩佩, 魏姗, 景花荣. Logistic回归和人工神经网络在鉴别诊断肺癌性胸腔积液中的应用研究[J]. 昆明医科大学学报, 2024, 45(10): 55-60. doi: 10.12259/j.issn.2095-610X.S20241009
Ruicheng LI, Hongwei QIAN, Yanni FAN, Peipei ZHAO, Shan WEI, Huarong JING. Application of Logistic Regression and Artificial Neural Networks in the Differential Diagnosis of LC-MPE[J]. Journal of Kunming Medical University, 2024, 45(10): 55-60. doi: 10.12259/j.issn.2095-610X.S20241009
Citation: Ruicheng LI, Hongwei QIAN, Yanni FAN, Peipei ZHAO, Shan WEI, Huarong JING. Application of Logistic Regression and Artificial Neural Networks in the Differential Diagnosis of LC-MPE[J]. Journal of Kunming Medical University, 2024, 45(10): 55-60. doi: 10.12259/j.issn.2095-610X.S20241009

Logistic回归和人工神经网络在鉴别诊断肺癌性胸腔积液中的应用研究

doi: 10.12259/j.issn.2095-610X.S20241009
基金项目: 国家自然科学基金资助项目(81772485)
详细信息
    作者简介:

    李锐成(1985~),男,河南商丘市,医学硕士,副主任技师,主要从事肿瘤临床检验诊断方面的研究工作

    通讯作者:

    景花荣,E-mail:43277715@qq.com

  • 中图分类号: R446.11

Application of Logistic Regression and Artificial Neural Networks in the Differential Diagnosis of LC-MPE

  • 摘要:   目的  应用Logistic回归分析方法和人工神经网络(artificial neural network,ANN)技术,评估血清(serum,S-)和胸腔积液(pleural effusion,P-)中的癌胚抗原(CEA)、铁蛋白(FRT)、神经元特异性稀醇化酶(NSE)、鳞状细胞癌相关抗原(SCC)、糖类抗原50(CA50)、糖类抗原125(CA125)和细胞角蛋白19片段(CY21-1)在鉴别肺癌性胸腔积液(malignant pleural effusion of lung cancer,LC-MPE)与良性胸腔积液(benign pleural effusion,BPE)中的应用价值,建立通过肿瘤标志物诊断LC-MPE的诊断模型。  方法  对临床初诊的LC-MPE和BPE患者的血清和胸腔积液肿瘤标志物结果进行分析,应用Logistic回归分析和ANN技术分别建立诊断LC-MPE的诊断模型。  结果  S-NSE、S-CY21-1、P-CEA和P-NSE4项指标被筛选出并用于建模,研究建立的诊断LC-MPE的Logistic回归模型的灵敏度为93.23%,特异度为97.46%,ROC曲线下面积为0.992。建立的ANN模型的灵敏度为95.35%,特异度为97.22%,ROC曲线下面积为0.990 (P < 0.05)。  结论  在通过肿瘤标志物诊断LC-MPE方面,建立的Logistic回归模型和ANN模型均有较好的诊断性能,上述2个模型均可辅助临床医生提高诊断准确率。
  • 图  1  Logistic和ANN模型诊断LC-MPE和BPE的ROC曲线

    Figure  1.  The ROC curves of Logistic and ANN model for differential diagnosis of LC-MPE and BPE

    表  1  2组胸腔积液患者基本资料[n(%)]

    Table  1.   Basic characteristics of patients with pleural effusion in 2 groups [n(%)]

    指标 LC-MPE组
    n=133)
    BPE组
    n=118)
    χ2/t P
    性别 3.098 0.078
     男 77(57.90) 81(68.64)
     女 56(42.10) 37(31.36)
    年龄 1.029 0.305
    (岁) 37~88 35~89
     ($\bar x \pm s $) 64.06±10.84 62.51±12.81
    下载: 导出CSV

    表  2  各单项肿瘤标志物在两组中的结果比较[M(P25,P75)]

    Table  2.   Comparison of results for individual tumor markers between the two groups [M(P25,P75)]

    指标 LC-MPE组(n=133) BPE组(n=118) Z P
    S-CEA (ng/mL) 9.70 (3.64,49.01) 1.54 (1.03,2.33) 10.155 <0.001*
    S-FRT (ng/mL) 179.00 (109.30,307.25) 299.85 (152.93,517.55) 4.443 <0.001*
    S-NSE (ng/mL) 17.84 (12.86,25.85) 13.79 (10.51,19.21) 4.450 <0.001*
    S-SCC (ng/mL) 0.48 (0.34,0.65) 0.46 (0.36,0.56) 1.227 0.220
    S-CA50 (U/mL) 8.16 (5.16,16.45) 5.44 (3.65,7.99) 4.841 <0.001*
    S-CA125 (U/mL) 99.89 (47.13,185.30) 76.25 (39.10,127.20) 2.682 0.007*
    S-CY21-1 (ng/mL) 5.64 (3.21,12.19) 1.83 (1.30,2.62) 10.946 <0.001*
    P-CEA (ng/mL) 125.90 (13.55,623.90) 1.21 (0.68,1.78) 11.486 <0.001*
    P-FRT (ng/mL) 1410.00 (883.65,2000.00 1138.00 (597.53,2000.00 2.414 0.016*
    P-NSE (ng/mL) 10.67 (5.51,30.69) 5.73 (2.99,10.74) 5.136 <0.001*
    P-SCC (ng/mL) 0.82 (0.57,1.81) 1.00 (0.65,2.03) 1.428 0.153
    P-CA50 (U/mL) 8.39 (3.58,42.95) 3.62 (2.11,5.93) 6.638 <0.001*
    P-CA125 (U/mL) 965.40 (451.85,1697.50 397.25 (177.90,804.38) 6.056 <0.001*
    P-CY21-1 (ng/mL) 87.28 (30.16,227.45) 10.23 (5.63,22.34) 10.126 <0.001*
      *P < 0.05。
    下载: 导出CSV

    表  3  各单项肿瘤标志物的诊断性能分析

    Table  3.   Analysis of the diagnostic performance of individual tumor markers

    指标 灵敏度(%) 特异度 (%) 阳性
    预测值(%)
    阴性
    预测值(%)
    准确率(%) 约登指数 AUC P 95%CI
    S-CEA 75.19 94.92 94.34 77.24 84.46 0.701 0.871 <0.001* 0.826~0.917
    S-FRT 81.95 42.37 61.58 67.57 63.35 0.243 0.663 <0.001* 0.595~0.730
    S-NSE 57.89 66.10 65.81 58.21 61.75 0.240 0.663 <0.001* 0.596~0.729
    S-CA50 51.88 76.27 71.13 58.44 63.35 0.282 0.677 <0.001* 0.612~0.743
    S-CA125 54.89 58.47 59.84 53.49 56.57 0.134 0.598 0.007* 0.528~0.668
    S-CY21-1 72.93 86.44 85.84 73.91 79.28 0.594 0.900 <0.001* 0.864~0.937
    P-CEA 84.96 98.31 98.26 85.29 91.24 0.833 0.920 <0.001* 0.880~0.961
    P-FRT 66.17 47.46 58.67 55.45 57.37 0.136 0.587 0.017* 0.516~0.659
    P-NSE 47.37 77.97 70.79 56.79 61.75 0.253 0.688 <0.001* 0.623~0.753
    P-CA50 56.39 84.75 80.65 63.29 69.72 0.411 0.743 <0.001* 0.682~0.803
    P-CA125 58.65 68.64 67.83 59.56 63.35 0.273 0.722 <0.001* 0.660~0.783
    P-CY21-1 63.91 89.83 87.63 68.83 76.10 0.537 0.870 <0.001* 0.828~0.913
      *P < 0.05。
    下载: 导出CSV

    表  4  肿瘤标志物的二元Logistic多因素分析

    Table  4.   Binary Logistic Multivariate Analysis of Tumor Markers

    指标 β SE Wald P OR 95%CI
    S-NSE 0.099 0.042 5.627 0.018* 1.104 (1.017~1.197)
    S-CY211 0.606 0.214 8.048 0.005* 1.833 (1.206~2.787)
    P-CEA 0.829 0.225 13.577 <0.001* 2.291 (1.474~3.562)
    P-NSE 0.037 0.017 4.738 0.030* 1.038 (1.004~1.073)
    常数 −7.900 1.503 27.645 <0.001* 0.000
      *P < 0.05。“−”表示无数据。
    下载: 导出CSV

    表  5  2种诊断模型的诊断性能比较

    Table  5.   Comparison of diagnostic performance of two diagnostic models

    诊断模型 灵敏度(%) 特异度(%) 阳性预测值(%) 阴性预测值(%) 约登指数 AUC P 95%CI
    Logistic 93.23 97.46 97.64 92.74 0.907 0.992 <0.001* 0.986~0.999
    ANN-测试集 90.63 98.37 98.31 90.98 0.890 0.992 <0.001* 0.985~0.999
    ANN-验证集 95.35 97.22 97.62 94.59 0.926 0.990 <0.001* 0.982~0.998
      *P < 0.05。
    下载: 导出CSV
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  • 收稿日期:  2024-05-20
  • 网络出版日期:  2024-11-07
  • 刊出日期:  2024-10-31

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