Objective To analyze risk factors of malignancy in patients with small pulmonary nodules (diameter ≤2 cm) using univariate analysis and multivariate logistic regression,and establish a mathematical prediction model to estimatethe probability of malignancy. Methods Clinical data of 147 patients with small pulmonary nodules who underwentsurgical resection with definite postoperative pathological diagnosis from January 2005 to September 2012 in the 161st Central Hospital of PLA were retrospectively analyzed. There were 84 male and 63 female patients with their age of 31-78(56.2±10.1) years. Univariate analysis using Chi-square test or t test was performed to analyze risk factors including patientage,gender,symptoms,history and quantity of smoking,history of heavy drinking,history of tumor,tumor site,diameter,lobulation,spiculation,pleural indentation,ground-glass opacity,cavity,enlarged hilar and mediastinal lymph nodes.Independent predictors of malignancy were screened with multivariate logistic regression analysis. A mathematical predictionmodel was built to estimate the probability of malignancy and then examined. Results Univariate analysis showed that there was statistical difference in patient age(t=7.146,P<0.001),heavy smoking history(χ2=6.169,P=0.013),nodule diameter(t=3.375,P=0.001),spiculation(χ2=5.609,P=0.018),lobulation(χ2=5.675,P=0.017),and pleural indentation(χ2=12.994,P<0.001)between benign and malignant small pulmonary nodule groups. Multivariate logistic regression analysis showed that patient age (OR=1.110,P=0.000),nodule diameter (OR=2.050,P=0.029),lobulation (OR=1.672,P=0.045),spiculation(OR=2.054,P=0.032) and pleural indentation(OR=4.090,P=0.024)were independent predictors of malignancy in patients with small pulmonary nodules (P<0.05) . The mathematical prediction model to estimate the probability of malignancy was:Logit (P) =ez/ (1 + ez),Z=-6.657 + (0.104×age) + (0.718×diameter) + (0.720×spiculation) +(0.514×lobulation) + (1.409×pleural indentation),and e was natural logarithm. Both Hosmer-Lemeshow test (χ2=1.802,P=0.986) and maximum likelihood ratio test (Cox-Snell R2=0.310,Nagelkerke R2=0.443) showed satisfactory goodness of fit. The diagnostic accuracy was 85.71%,sensitivity was 87.50%,specificity was 81.40%,positive predictive value was 91.92%,and negative predictive value was 72.92% when the cut-off value was 0.58. Conclusions Patient age,nodule diameter,spiculation,lobulation and pleural indentation are independent predictors of malignancy in patients with small pulmonary nodules. The mathematical prediction model can accurately estimate the probability of malignancy for patients with small pulmonary nodules.
Citation:
YAN Sijun,CAO Xiang,DENG Borong,XIE Nianlin,QIAODecheng,LIU Yan.. Risk Factor Analysis and Establishment of a Mathematical Prediction Model to Evaluate the Probability of Malignancy in Patients with Small Pulmonary Nodules. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2013, 20(4): 441-445. doi: 10.7507/1007-4848.20130134
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Copyright © the editorial department of Chinese Journal of Clinical Thoracic and Cardiovascular Surgery of West China Medical Publisher. All rights reserved
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黄萌, 陈星, 邱月锋, 等. 肺癌危险因素及交互作用研究. 中华疾病控制杂志, 2011, 15 (2):91-94.
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- 1. Wahidi MM, Govert JA, Goudar RK, et al. Evidence for the treatment of patients with pulmonary nodules:when is it lung cancer ?ACCP evidence-based clinical practice guidelines (2nd edition) Chest, 2007, 132 (3 Suppl):94S-107S.
- 2. 陈万青, 张思维, 郑荣寿, 等. 中国肿瘤登记地区2007年肿瘤发病和死亡分析. 中国肿瘤, 2011, 20 (3):162-169.
- 3. 陈陶阳, 姚红玉. 肺癌发病风险因素与预防对策. 中国预防医学杂志, 2011, 12 (9):809-811.
- 4. Ettinger DS, Akerley W, Bepler G, et al. Non-small cell lung cancer. J Natl Compr Canc Ne, 2008, 6 (3):228-269.
- 5. 姚晓军, 刘伦旭, 张洪伟, 等. 2000年与2010年四川大学华西医院肺癌临床诊治特征的对比研究. 中国肺癌杂志, 2012, 15 (6):355-360.
- 6. Okami J, Ito Y, Higashiyama M, et al. Sublobar resection provides an equivalent survival after lobectomy in elderly patients with early lung cancer. Ann Thorac Surg, 2010, 90 (5):1651-1656.
- 7. Vazquez M, Carter D, Brambilla E, et al. Solitary and multiple resected adenocarcinomas after CT screening for lung cancer:histopathologic features and their prognostic implications. Lung Cancer, 2009, 64 (2):148-154.
- 8. Varoli F, Vergani C, Caminiti R, et al. Management of solitary pulmonary nodule. Eur J Cardiothorac Surg, 2008, 33 (3):461-465.
- 9. 吴晓华, 马大庆, 张忠嘉, 等. 多层螺旋CT胸部低剂量扫描发现肺结节的临床研究. 中华放射学杂志, 2004, 38 (7):767-770.
- 10. 徐勤, 敬文斌, 陈永芊, 等. 低剂量螺旋CT对肺癌高危人群筛查的价值. 现代肿瘤医学, 2012, 20 (3):530-532.
- 11. Iwano S, Makino N, Ikeda M, et al. Videotaped helical CT images for lung cancer screening. J Comput Assist Tomogr, 2000, 24 (2):242-246.
- 12. Cronin P, Dwamena BA, Kelly AM, et al. Solitary pulmonary nodules and masses:a meta-analysis of the diagnostic utility of alternative imaging tests. Eur Radiol, 2008, 18 (9):1840-1856.
- 13. 贾守勤, 尚延海, 赵斌. 肺结节的影像学研究进展. 医学影像学杂志, 2009, 19 (2):230-233.
- 14. 王华斌, 李苏建, 卢光明. 多层螺旋CT评估孤立性肺结节的临床研究进展. 放射学实践, 2010, 25 (1):105-108.
- 15. Soubani AO. The evaluation and management of the solitary pulmonary nodule. Postgrad Med J, 2008, 84 (995):459-466.
- 16. Li Y, Chen KZ, Wang J. Development and validation of a clinical prediction model to estimate the probability of malignancy in solitary pulmonary nodules in Chinese People. Clin Lung Cancer, 2011, 12 (5):313-319.
- 17. 欧兆荣, 陶连琴, 时国朝, 等. 孤立性肺结节的影像学特征及两种肺癌预测模型的比较. 中国呼吸与危重监护杂志, 2012, 11 (2):168-171.
- 18. 李运, 陈克终, 隋锡朝, 等. 孤立性肺结节良恶性判断数学预测模型的建立. 北京大学学报:医学版, 2011, 43 (3):450-454.
- 19. 王涛, 陈宝俊, 范晓红, 等. 肺微小结节的临床诊断与治疗. 中国胸心血管外科临床杂志, 2012, 19 (3):274-279.
- 20. 黄萌, 陈星, 邱月锋, 等. 肺癌危险因素及交互作用研究. 中华疾病控制杂志, 2011, 15 (2):91-94.