A prediction model to evaluate the pretest risk of malignancy in solitary pulmonary nodules: evidence from a large Chinese southwestern population
A prediction model to evaluate the pretest risk of malignancy in solitary pulmonary nodules: evidence from a large Chinese southwestern population
复制标题
评估孤立性肺结节恶性肿瘤预测风险的预测模型：来自中国西南地区大量人群的证据
DOI：
10.1007/s00432-020-03408-2
复制
发表时间：
2020-10-06
影响因子：
3.6
通讯作者：
Chen, Bojiang
中科院分区：
文献类型：
--
作者：
Wu, Zuohong;Huang, Tingting;Chen, Bojiang
目的肺癌是癌症死亡的主要原因，已有临床预测模型。本研究旨在评估已发表模型的诊断性能，并创建新的模型来评估中国人群中恶性孤立性肺结节（SPN）的概率。方法回顾性分析2008年1月至2016年12月在华西医院收治的2061例孤立性肺结节患者的临床资料，每例孤立性肺结节均经病理证实。首先，对四种已发表的预测模型，即马约诊所模型、退伍军人事务部（VA）模型、布洛克模型和北京大学人民医院（PEH）模型进行了验证。然后，利用逻辑回归，决策树和随机森林（RF），我们开发了三个新的模型，并在内部验证它们。结果4个已发表模型的受试者工作特征曲线下面积（AUC）值如下：马约0.705（95%CI 0.658- 0.752，n = 726），VA 0.64 6（95%CI 0.598- 0.695，n = 800）、Brock 0.575（95%CI 0.502- 0.648，n = 550）和PEH 0.675（95%CI 0.627- 0.723，n = 726）。建立Logistic回归模型、决策树模型和RF模型，其AUC值分别为0.842（95% CI 0.778-0.906）、0.734（95% CI 0.647-0.821）、0.851（95% CI 0.789-0.914）。结论已发表的4种肺癌预测模型均不适用于我国人群，我们建立了新的模型，可用于预测恶性SPN的概率。
Purpose Lung cancer is the leading cause of cancer death and there have been clinical prediction models. This study aimed to evaluate the diagnostic performance of published models and create new models to evaluate the probability of malignant solitary pulmonary nodules (SPNs) in Chinese population. Methods We consecutively enrolled 2061 patients with SPNs from West China Hospital between January 2008 and December 2016, each SPN was pathologically confirmed. First, four published prediction models, Mayo clinic model, Veterans Affairs (VA) model, Brock model and People's Hospital of Peking University (PEH) model were validated in our patients. Then, utilizing logistic regression, decision tree and random forest (RF), we developed three new models and internally validated them. Results Area under the receiver operating characteristic curve (AUC) values of four published models were as follows: Mayo 0.705 (95% CI 0.658-0.752,n = 726), VA 0.64 6 (95% CI 0.598-0.695,n = 800), Brock 0.575 (95% CI 0.502-0.648,n = 550) and PEH 0.675 (95% CI 0.627-0.723,n = 726). Logistic regression model, decision tree model and RF model were developed, AUC values of these models were 0.842 (95% CI 0.778-0.906), 0.734 (95% CI 0.647-0.821), 0.851 (95% CI 0.789-0.914), respectively. Conclusion The four published lung cancer prediction models do not apply to our population, and we have established new models that can be used to predict the probability of malignant SPNs.