A Predicting Model to Estimate the Probability of Malignancy in Solitary Pulmonary Nodules Basing on CT Images
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摘要: 目的：筛选并分析影响肺结节良恶性的因素，建立预测模型、验证该模型并与梅奥模型、Brock模型对比。方法：回顾性分析2015年1月至2017年12月深圳市人民医院有病理结果的孤立性肺结节病例319例，其中229例作为建模组（A组），90例作为验证组（B组），分析A组病例性别、年龄、直径、吸烟史、毛刺、位于上叶、边界不清楚、分叶征、空泡征、血管集束征、胸膜凹陷征、含磨玻璃密度及钙化，通过单因素分析及Logistic回归分析，筛选出独立影响因子，并建立回归方程。用B组资料进行验证并将B组资料分别代入本研究模型、梅奥模型及Brock模型进行对比。结果：单因素分析示年龄、直径、毛刺、上叶、边界不清楚、分叶、空泡、血管集束征、胸膜凹陷征、是否含有磨玻璃密度在良恶性结节中的差异具有统计学意义（P<0.05），Logistic回归分析示有毛刺、有分叶、边界不清楚和含有磨玻璃密度为恶性孤立性肺结节的独立影响因素，并据此建立的回归方程ROC曲线下面积为0.894，其灵敏度为91.3％，特异度为77.3％，阳性似然比为4.02，阴性似然比为0.11，阳性预测值为80.8％，阴性预测值为89.5％；本研究模型与梅奥模型的差异有统计学意义（P=0.0049），与Brock模型差异没有统计学意义（P=0.79）。结论：有毛刺、有分叶、边界不清楚和含有磨玻璃密度为恶性孤立性肺结节的独立影响因素，据此建立的回归方程具有较高的诊断效能。本研究建立的模型诊断效能优于梅奥模型，与Brock模型诊断效能相当。
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关键词:
- 孤立性肺结节 /
- CT /
- logistic回归分析
Abstract: Objective: To establish a predicting model using multivariate logistic regression analysis for estimating the probability of malignancy in solitary pulmonary nodules, and to compare our model with Mayo model and Brock model. Methods: From January 2015 to December 2017, 319 patients with SPNs identified by histopathology in Shenzhen peoples' hospital were analyzed retrospectively. Among 319 cases, 229 patients were in modeling group (group A), and 90 patients were in validating group (group B). We analyzed gender, age, diameter, smoking history, spiculation, upper location, unclear border, lobulation, vacuole sign, vessel convergence sign, pleural indentation, ground glass opacity and calcification in patients of group A, selected independent influencing factors by univariate analysis and multivariate logistic regression analysis and established a predicting model. Our model was verified with the date of group B, and was compared with Mayo model and Brock model. Results: The age, diameter, upper location, unclear border, lobulation, vacuole sign, vessel convergence sign, pleural indentation, and ground glass opacity were shown statistically significance between malignant and benign SPNs in univariate analysis (P<0.05). The spiculation, unclear border, lobulation, and ground glass opacity were independent influencing factors in multivariate logistic regression analysis. When group B data was substituted into the established formula, the area under the ROC curve was 0.894, sensitivity was 91.3%, specificity was 77.3%, positive likely ratio was 4.02, negative likely ratio was 0.11, positive predictive value was 80.8%, negative predictive was 89.5%. The difference between our model and Mayo model was statistically significant (P=0.0049). The difference between our model and Brock model was not statistically significant (P=0.79). Conclusion: The spiculation, unclear border, lobulation, and ground glass opacity are independent influencing factors between benign and malignant solitary pulmonary nodules. This logistic regression equation has favorable effective functions for the diagnosis of SPNs. For patients in this study, our model is better than Mayo model, and is same as Brock model.-
Keywords:
- solitary pulmonary nodule /
- CT /
- logistic regression analysis
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期刊类型引用(11)
1. 刘军旗，钱伟军，李立，赵文，王亚军，杨洁. 良恶性肺结节影像学特征及定量参数的鉴别诊断价值. 中国医学工程. 2024(03): 25-30 . 
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3. 林红东，马伟琼，陈镜聪，周玉祥. 实性结节型早期肺癌相关因素分析及模型比较. 放射学实践. 2024(11): 1453-1458 . 百度学术
4. 俞璐. Brock模型及肺部结节CT报告分级系统对肺结节良恶性的鉴别诊断价值. 中国基层医药. 2023(12): 1838-1842 . 百度学术
5. 邓红梅，郑淋栖，吴东洋，罗强，罗新民. 孤立性肺结节患者的X线、MRI诊断影像特点观察. 中国CT和MRI杂志. 2022(09): 58-59 . 百度学术
6. 熊立军，李超平，周星辉，漆蕾. 能谱CT在孤立性肺结节定性诊断中的临床效能. 医疗装备. 2022(16): 16-18 . 百度学术
7. 李红英，胡鑫，宋瑞祥. 高分辨CT影像学特征对孤立性肺结节良恶性的鉴别诊断效能. 海南医学. 2022(19): 2540-2543 . 百度学术
8. 张厚丽，罗虎，王康，陈俞坊，衣杏林，周向东. 采用双能CT构建肺结节良恶性预测模型及碘图定量参数的临床分析. 中华肺部疾病杂志(电子版). 2022(05): 630-636 . 百度学术
9. 黄杰. 螺旋CT结合肿瘤标记物对孤立性肺结节良恶性判断的临床研究及对肺结节良恶性生长变化的对比分析. 影像研究与医学应用. 2021(04): 157-158 . 百度学术
10. 樊呈强，郑次浩，伍冠生. CT下钙化特点对肺结节良恶性鉴别诊断的临床价值分析. 影像研究与医学应用. 2021(10): 181-182 . 百度学术
11. 邹正荣. 孤立性肺结节CT诊断与鉴别诊断. 影像研究与医学应用. 2020(24): 178-180 . 百度学术
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