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| 孤立性肺结节良恶性判别数学模型的建立与验证 |
| 杨娟1,孙雪丽1,赖国祥2,余晖3,李强1,韩一平1* |
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(1. 第二军医大学长海医院呼吸内科, 上海 200433;
2. 南京军区福州总医院呼吸内科, 福州 350025;
3. 厦门大学福州第二医院呼吸内科, 福州 350007
*通信作者) |
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| 摘要: |
| 目的 分析筛选出与孤立性肺结节(solitary pulmonary nodules, SPN)恶性概率相关的一组临床资料,建立并验证了SPN良恶性判别的数学模型,并将该模型与国内李运模型和国外Mayo模型、VA模型进行比较。方法 分别收集2011年1月至2014年11月在第二军医大学长海医院手术切除并明确病理的资料252例,总结性别、年龄、症状、吸烟史、肺部基础疾病史、既往肿瘤家族史、结节部位、最大直径、边界清楚、边缘光滑、毛刺、分叶、棘突、胸膜凹陷征、血管集束征、透亮影等资料。从252例资料中选出83例作为验证组(B组),剩余169例作为建模组(A组);同时从B组数据中剔出6例使得其剩余的77例数据均符合其他3个模型的入选和排除条件并组成C组。通过Logistic分析A组资料筛选出与SPN良恶性相关的5个独立因子,构建良恶性概率判别模型。并用B组验证本文模型、C组分别对四个模型进行统一验证和比较。结果 年龄、既往肿瘤史、最大直径、钙化、透亮影这5项因素的差异在良性和恶性SPN之间有统计学意义(P<0.05)。建立的SPN良恶性概率数学判别方程,将B组数据代入公式,得出的model ROC (receiver operating characteristic)曲线下面积(AUC)为 0.905±0.036,灵敏性为79.3%、特异性为84.0%、阳性似然比为4.957、阴性似然比为 0.246、阳性预测值为0.920、阴性预测值0.636。将C组数据验证长海模型AUC为0.893±0.040,李运模型AUC为0.817±0.056,Mayo模型AUC为0.804±0.050,VA模型AUC为0.780±0.057。结论 患者年龄、肿瘤史、结节最大直径、钙化、透亮影是SPN良、恶性判别的独立预测因子,通过Logistic回归建立的数学模型有一定的临床应用价值。对于本研究的患者病例,长海模型比李运模型、Mayo模型、VA模型预测效果都更有效。 |
| 关键词: 孤立性肺结节 logistic模型 肺肿瘤 临床病理学 |
| DOI：10.3724/SP.J.1008.2015.00407 |
| 投稿时间：2014-09-23修订日期：2015-03-02 |
| 基金项目: |
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| Establishment and validation of mathematics model for differentiating benign and malignant solitary pulmonary nodules |
| YANG Juan1,SUN Xue-li1,LAI Guo-xiang2,YU Hui3,LI Qiang1,HAN Yi-ping1* |
(1. Department of Respiratory Medicine, Changhai Hospital, Second Military Medical University, Shanghai 200433, China;
2. Department of Respiratory Medicine, Fuzhou General Hospital, PLA Nanjing Military Area Command, Fuzhou 350025, Fujian, China;
3. Department of Respiratory Medicine, Second Hospital of Fuzhou, Xiamen University, Fuzhou 350007, Fujian, China
*Corresponding author) |
| Abstract: |
| Objective To establish a prediction model by multivariate logistic regression analysis for estimating the malignant probability of solitary pulmonary nodules (SPNs), and to compare our model with other models. Methods From January 2011 to November 2014, totally 252 patients with SPNs who had undergone pneumonectomies in Thoracic Surgery Department of Changhai Hospital and been confirmed with definite pathological results were included in this retrospective study. The gender, age, symptom, smoking history, history of pulmonary diseases, history of tumor, family history of tumor, the location of lesion, maximum diameter, clear border, smooth border, spiculation, lobulation, spinous protuberant sign, pleural indentation, calcification, vessel convergence sign, and lucency shadow were all reviewed. Eighty-three cases were designated as validation group (group B), and the remaining 169 cases were taken as the modeling group (group A). Six cases were excluded from group B to meet all the inclusion and exclusion criteria of the other three models, and the remaining 77 cases constituted group C. Logistic analysis identified five independent factors associated with malignant probability of SPNs from group A and a clinical prediction model was built. With the data of group B and group C, this model was verified and was compared with the other three classical models. Results The age, history of tumor, maximum diameter, calcification, and lucency shadow were the five factors identified for differentiating benign and malignant SPNs (P<0.05). When group B data was substituted into the established formula, the area under curve (AUC) of the ROC was 0.905±0.036, sensitivity was 79.3%, specificity was 84.0%, positive likelihood ratio was 4.957, negative likelihood ratio was 0.246, positive predictive value was 0.920, and negative predictive value was 0.636. When the data of group C were fed to the four formulas of Changhai, Li Yun, Mayo and VA models, the corresponding AUCs were 0.893±0.040, 0.817±0.056, 0.804±0.050, and 0.780±0.057, respectively. Conclusion The patient's age, history of tumor, maximum diameter, calcification, and lucency shadow are the independent predictors of malignant probability of SPNs. This logistic regression prediction mathematic model is of clinical application value. For patients in this study, our Changhai model seems to work better than the Li Yun, Mayo,and VA model. |
| Key words: solitary pulmonary nodule logistic models lung neoplasms clinical pathology |
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