朱颖, 许攀峰, 姚亚克, 潘建芳, 周建英. 性孤立性肺微小结节的独立预测因子及预测模型[J]. 中国肿瘤临床, 2018, 45(10): 497-502. DOI: 10.3969/j.issn.1000-8179.2018.10.162
引用本文: 朱颖, 许攀峰, 姚亚克, 潘建芳, 周建英. 性孤立性肺微小结节的独立预测因子及预测模型[J]. 中国肿瘤临床, 2018, 45(10): 497-502. DOI: 10.3969/j.issn.1000-8179.2018.10.162
Zhu Ying, Xu Panfeng, Yao Yake, Pan Jianfang, Zhou Jianying. Independent predictors and prediction model of malignant micro-sized solitary pulmonary nodules[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2018, 45(10): 497-502. DOI: 10.3969/j.issn.1000-8179.2018.10.162
Citation: Zhu Ying, Xu Panfeng, Yao Yake, Pan Jianfang, Zhou Jianying. Independent predictors and prediction model of malignant micro-sized solitary pulmonary nodules[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2018, 45(10): 497-502. DOI: 10.3969/j.issn.1000-8179.2018.10.162

性孤立性肺微小结节的独立预测因子及预测模型

Independent predictors and prediction model of malignant micro-sized solitary pulmonary nodules

  • 摘要:
      目的  对恶性孤立性肺微小结节(solitary pulmonary nodule,SPN)(直径≤10 mm)进行回顾性分析,找出微小SPN的独立预测因子并建立预测模型。
      方法  选取2012年6月至2014年3月浙江大学附属第一医院102例有明确病理诊断微小SPN患者(A组)的临床数据,进行单因素、二元Logistic回归分析,总结出恶性微小SPN的独立预测因子并建立预测模型。另外收集浙江金华广福医院2015年1月至2017年8月行微小SPN手术并取得病理诊断结果的患者10例(B组),验证本模型的诊断效能,并与经典模型梅奥诊所模型(Mayo Clinic Model)进行对比。
      结果  102例患者(A组)平均年龄为(55.31±10.77)岁;其中75.5%为恶性,24.5%为良性。无临床症状、CT影像结节位于上肺、结节直径 > 5 mm、结节边界不清楚、非规则球形、无钙化,上述6项因素为恶性微小SPN的独立预测因子。建立预测模型ROC曲线下面积为0.922,95%CI:0.857~0.986,对应的诊断敏感性为88.3%,特异性为84.0%。比梅奥诊所模型的诊断效果好。
      结论  本研究总结了恶性微小SPN的独立预测因子,预测模型能较好预测恶性微小SPN。

     

    Abstract:
      Objective  To evaluate the clinical factors affecting the probability of malignant micro-sized (≤10 mm) solitary pulmonary nodules (≤10 mm, micro-sized SPN), and established a clinical prediction model.
      Methods  Medical records from 102 patients with a pathological diagnosis of micro-sized SPN (Group A), established between June 2012 and March 2014, were reviewed. Clinical data were collected to evaluate the independent predictors of malignant micro-sized SPN using single factor analysis and Logistic regression analysis. A clinical prediction model was subsequently created. Receiver-operating characteristic (ROC) curves were constructed using the prediction model. Between January 2015 and August 2017, data from an additional 10 patients enrolled from January 2015 to August 2017 from Jinhua Guangfu Hospital (Group B) with a pathologically diagnosed micro-sized SPN were used to validate this clinical prediction model. The model was also compared with the Mayo Clinic Model.
      Results  Median age of 102 patients (Group A) was 55.31±10.77 years old. There were 75.5% malignant nodules and 24.5% benign ones. Logistic regression analysis identified six clinical characteristics (no symptoms, upper lobe, diameter > 5 mm, no clear border, not irregular round, no calcification) as independent predictors of malignancy in patients with micro-sized SPN. The area under the ROC curve for our model was 0.922 (95% CI: 0.857-0.986). In our model, the diagnosis sensitivity and specificity were 88.3% and 84.0%, respectively. The test power of the model was better compared with the Mayo Clinic Model.
      Conclusions  In this study, we had found the independent predictors of malignant micro-sized SPN, and developed a prediction model that could accurately identify malignant micro-sized SPN in patients.

     

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