留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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
  • [1] Kim N Y,Jang B,Gu K M,et al. Differential diagnosis of pleural effusion using machine learning[J]. Ann Am Thorac Soc,2024,21(2):211-217. doi: 10.1513/AnnalsATS.202305-410OC
    [2] Gonnelli F,Hassan W,Bonifazi M,et al. Malignant pleural effusion: current understanding and therapeutic approach[J]. Respir Res,2024,25(1):47. doi: 10.1186/s12931-024-02684-7
    [3] Han Y Q,Yan L,Li P,et al. A study investigating markers in pleural effusion (SIMPLE): A prospective and double-blind diagnostic study[J]. BMJ Open,2019,9(8):e27287.
    [4] Brun C,Gay P,Cottier M,et al. Comment from the authors: the tests combination in patients with lung cancer and malignant pleural effusion[J]. J Thorac Dis,2019,11(5):E74-E75. doi: 10.21037/jtd.2019.05.27
    [5] Liu Q,Yu Y X,Wang X J,et al. Diagnostic accuracy of interleukin-27 between tuberculous pleural effusion and malignant pleural effusion: A meta-analysis[J]. Respiration,2018,95(6):469-477. doi: 10.1159/000486963
    [6] Da C L V,Ribeiro-Alves M,Da S C R,et al. Predominance of Th1 immune response in pleural effusion of patients with tuberculosis among other exudative etiologies[J]. J Clin Microbiol,2019,58(1):e919-e927.
    [7] 中华医学会呼吸病学分会胸膜与纵隔疾病学组. 胸腔积液诊断的中国专家共识[J]. 中华结核和呼吸杂志,2022,45(11):1080-1096. doi: 10.3760/cma.j.cn112147-20220511-00403
    [8] Gonnelli F,Hassan W,Bonifazi M,et al. Malignant pleural effusion: Current understanding and therapeutic approach[J]. Respir Res,2024,25(1):1-11. doi: 10.1186/s12931-023-02626-9
    [9] Wu Q,Li M,Zhang S,et al. Clinical diagnostic utility of CA 15-3 for the diagnosis of malignant pleural effusion: A meta-analysis[J]. Exp Ther Med,2015,9(1):232-238. doi: 10.3892/etm.2014.2039
    [10] Nguyen A H,Miller E J,Wichman C S,et al. Diagnostic value of tumor antigens in malignant pleural effusion: A meta-analysis[J]. Transl Res,2015,166(5):432-439. doi: 10.1016/j.trsl.2015.04.006
    [11] Charakorn C,Thadanipon K,Chaijindaratana S,et al. The association between serum squamous cell carcinoma antigen and recurrence and survival of patients with cervical squamous cell carcinoma: A systematic review and meta-analysis[J]. Gynecol Oncol,2018,150(1):190-200. doi: 10.1016/j.ygyno.2018.03.056
    [12] Ma Q,Liu W,Jia R,et al. Inflammation-based prognostic system predicts postoperative survival of esophageal carcinoma patients with normal preoperative serum carcinoembryonic antigen and squamous cell carcinoma antigen levels[J]. World J Surg Oncol,2016,141(14):1-6.
    [13] 李锐成,郜赵伟,董轲,等. 胸腔积液与血清中的癌胚抗原及其比值对结核性与肺癌性胸腔积液的诊断价值[J]. 南方医科大学学报,2019,39(2):175-180.
    [14] Son S M,Han H S,An J Y,et al. Diagnostic performance of CD66c in lung adenocarcinoma-associated malignant pleural effusion: Comparison with CEA,CA 19-9,and CYFRA 21-1[J]. Pathology,2015,47(2):123-129. doi: 10.1097/PAT.0000000000000215
    [15] Shin Y M,Yun J,Lee O J,et al. Diagnostic value of circulating extracellular miR-134,miR-185,and miR-22 Levels in lung adenocarcinoma-associated malignant pleural effusion[J]. Cancer Res Treat,2014,46(2):178-185. doi: 10.4143/crt.2014.46.2.178
    [16] Abbas M,Kassim S A,Habib M,et al. Clinical evaluation of serum tumor markers in patients with advanced-stage non-small cell lung cancer treated with palliative chemotherapy in China[J]. Front Oncol,2020,10(6):1-12.
    [17] Heijnen B J,Bohringer S,Speyer R. Prediction of aspiration in dysphagia using logistic regression: oral intake and self-evaluation[J]. Eur Arch Otorhinolaryngol,2020,277(1):197-205. doi: 10.1007/s00405-019-05687-z
    [18] Chiou S H,Betensky R A,Balasubramanian R. The missing indicator approach for censored covariates subject to limit of detection in logistic regression models[J]. Ann Epidemiol,2019,38(10):57-64.
    [19] Teglia C M,Guinez M,Goicoechea H C,et al. Enhancement of multianalyte mass spectrometry detection through response surface optimization by least squares and artificial neural network modelling[J]. J Chromatogr A,2020,1611(40):460613.
    [20] Yogeswari M K,Dharmalingam K,Mullai P. Implementation of artificial neural network model for continuous hydrogen production using confectionery wastewater[J]. J Environ Manage,2019,252(20):109684.
    [21] Chen Y C,Chang Y C,Ke W C,et al. Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data: An example for non-small cell lung cancer[J]. J Biomed Inform,2015,56(4):1-7.
    [22] Ligor T,Pater L,Buszewski B. Application of an artificial neural network model for selection of potential lung cancer biomarkers[J]. J Breath Res,2015,9(2):27106. doi: 10.1088/1752-7155/9/2/027106
  • [1] 王缨, 傅聪, 傅颖.  肺癌患者血清LDH、CysC、PWR水平检测意义, 昆明医科大学学报. doi: 10.12259/j.issn.2095-610X.S20240424
    [2] 张江, 赵喜娟, 吴江, 杨秉坤, 杨妮, 周丽萍.  肺癌放疗患者衰弱现状及影响因素分析, 昆明医科大学学报.
    [3] 张洪波, 李振龙, 吕瑛, 张益绰, 裘翔铭, 黄婷婷.  单孔与双孔电视胸腔镜肺叶切除术治疗肺癌的临床疗效比较, 昆明医科大学学报. doi: 10.12259/j.issn.2095-610X.S20240419
    [4] 吕永昌, 罗伟, 张旭刚, 曹然, 李自强, 王荣, 郑啟颖, 刘跃昆, 刘廷艳, 尹吉利, 丁沛沛, 王昆.  单孔胸腔镜肺术后联合康复科干预对患者康复的影响, 昆明医科大学学报. doi: 10.12259/j.issn.2095-610X.S20221013
    [5] 王燕, 丁荣, 张吕玲, 王若花, 赵晓玲, 马娜.  输血治疗联合放化疗在结直肠癌患者中的疗效及对肿瘤标志物和T淋巴细胞水平的影响, 昆明医科大学学报. doi: 10.12259/j.issn.2095-610X.S20220809
    [6] 姚璐, 谭慧, 阮永华, 钱忠义, 杨志鸿, 阮锐, 张琦颖, 周明婷, 方星尹.  超声支气管镜引导下的经支气管针吸活检术在肺和纵隔占位性病变诊断中的应用, 昆明医科大学学报. doi: 10.12259/j.issn.2095-610X.S20210311
    [7] 马燕粉, 胡建, 张宁, 武倩, 王晓琴.  肺腺癌性与结核性胸腔积液患者凝血指标变化及异常模式, 昆明医科大学学报. doi: 10.12259/j.issn.2095-610X.S20211021
    [8] 武江海, 舒敬奎, 张剑青, 冯家钢, 贾曼, 刘凌.  结核与肿瘤患者胸腔积液中TNF-α,IFN-γ,IL-2,IL-4的水平及临床意义, 昆明医科大学学报.
    [9] 罗劭蕾, 马丽菊, 马腾飞, 邵文萍.  多种肿瘤标志物联合检测对大肠癌临床诊断的价值, 昆明医科大学学报.
    [10] 王洪.  前列腺素E2对COPD合并肺癌的致病机理, 昆明医科大学学报.
    [11] 杨少华.  血清多种肿瘤标记物联合磁共振成像在胰腺癌中的诊断价值, 昆明医科大学学报.
    [12] 潘龙芳.  PDCA管理方法在肺癌患者PICC质量控制中的应用, 昆明医科大学学报.
    [13] 舒敬奎.  内科胸腔镜在结核性包裹性胸腔积液诊治中的应用, 昆明医科大学学报.
    [14] 党勇.  64排CT联合血清学检查对非小细胞性肺癌纵隔淋巴结转移的诊断, 昆明医科大学学报.
    [15] 杜兴华.  ALX4基因在肿瘤中的甲基化与表达, 昆明医科大学学报.
    [16] 李定彪.  C-12蛋白芯片检测宣威地区肺癌的临床意义, 昆明医科大学学报.
    [17] 陈锦润.  3,4-苯并芘支气管灌注构建猪肺癌模型的实验研究, 昆明医科大学学报.
    [18] 马建强.  全胸腔镜微创肺癌根治术手术创伤的临床研究, 昆明医科大学学报.
    [19] 梁咏雪.  可弯曲内科胸腔镜在诊断不明原因胸腔积液中的应用, 昆明医科大学学报.
    [20] 芮桥安.  血清肿瘤标志物联检在结肠直肠癌诊断中的价值, 昆明医科大学学报.
  • 加载中
图(1) / 表(5)
计量
  • 文章访问数:  373
  • HTML全文浏览量:  252
  • PDF下载量:  11
  • 被引次数: 0
出版历程
  • 收稿日期:  2024-05-20
  • 网络出版日期:  2024-11-07
  • 刊出日期:  2024-10-31

目录

    /

    返回文章
    返回