利用常见血清肿瘤标志物构建肺癌病理分型的风险预测模型
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新疆医科大学附属肿瘤医院人文社会科学改革与发展专项(编号:2019SK02003)


Established the Risk Prediction Modeles for Pathological Classification of Lung Cancer by Common Serum Tumor Markers
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    摘要:

    目的:联合运用六种常见血清肿瘤标志物建立预测肺癌常见病理分型的风险模型。方法:回顾性分析新疆医科大学附属肿瘤医院 2012年5月至2013年5月收治的342例肺癌患者和91例肺部影像学可疑并确诊非肺癌患者的临床资料。对六种肿瘤标志物进行热图分析,得到肿瘤标志物和肺癌各病理亚型的关系。Logistic 回归分析筛选各病理亚型独立预测指标,并构建预测模型。通过受试者工作特性(ROC)曲线下面积(AUC)评估各个模型的预测能力。运用 Bootstrap 重抽样法对模型的预测能力进行内部验证。结果:热图分析结果显示Pro-GRP和NSE预测小细胞肺癌,SCC和CYFRA21-1预测肺鳞癌,CA125和CEA预测肺腺癌的组合更好。小细胞肺癌、肺鳞癌和肺腺癌预测模型对应的AUC分别为0.938,0.965和0.965。模型预测能力的内部验证表明三个模型预测效果理想。结论:血清肿瘤标志物联合患者一般资料构建针对肺癌患者病理分型的风险预测模型预测效果较好,未来进一步验证后可以展示在检验报告单上,为临床医生进一步诊疗提供帮助。

    Abstract:

    A bstract: Objective To establish a risk model for predicting the common pathological classification of lung cancer by the combination of six serum tumor markers. Methods The clinical data of 342 patients with lung cancer and 91 patients with suspicious lung imaging and diagnosed as non-lung cancer, who were treated in the Affiliated Cancer Hospital of X injiang Medical University during May 2012 and May 2013, were analyzed retrospectively. The relationship between tumor markers and pathological subtypes of lung cancer was obtained by the heat map analysis of 6 tumor markers. The independent predictive indicators of each pathological subtype were screened by the Logistic regression analysis, and a prediction model was constructed. The predictive ability of each model was evaluated by the area under the ROC curve (AUCROC ). The Bootstrap resampling method was used to validate the predictive ability of each model. Results The heat map analysis showed that the combination of pro-GRP and NSE was better in predicting small cell lung cancer, SCC and CY-FRA21-1 in predicting lung squamous cell carcinoma, and CA125 and CEA in predicting lung adenocarcinoma. The areas under the ROC curves of prediction models for small-cell lung cancer, lung squamous carcinoma and lung adenocarcinoma were 0.938, 0.965 and .0.965, respectively. Internal validation of the predictive ability of the three models showed that their predictive effects were ideal. Conclusion The risk prediction models for pathological lassifcation of lung cancer patients constucted by serum tumor markers combined with general data of patients have good predictive ffects. After further venification in the future, they could be displayed on the test report to provide help for clinicians in further diagnosis and treatment.

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王森钰,赖良,冯阳春.利用常见血清肿瘤标志物构建肺癌病理分型的风险预测模型[J].临床检验杂志,2022,40(03):200-203

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  • 收稿日期:2021-08-20
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  • 在线发布日期: 2022-06-08
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