Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules
10.3779/j.issn.1009-3419.2016.10.12
- VernacularTitle:实性孤立性肺结节诊断模型的建立
- Author:
YU WEI
1
;
YE BO
;
XU LIYUN
;
WANG ZHAOYU
;
LE HANBO
;
WANG SHANJUN
;
CAO HANBO
;
CHAI ZHENDA
;
CHEN ZHIJUN
;
LUO QINGQUAN
;
ZHANG YONGKUI
Author Information
1. 温州医科大学附属舟山医院胸心外科
- Keywords:
Solitary pulmonary nodules (SPNs);
Prediction model;
Independent predictors
- From:
Chinese Journal of Lung Cancer
2016;19(10):705-710
- CountryChina
- Language:Chinese
-
Abstract:
Background and objective hTe solitary pulmonary nodule (SPN) is a common and challenging clini-cal problem, especially solid SPN. hTe object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs.Methods We had a retrospective review of 317 sol-id SPNs (group A) having a ifnal diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, letf or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air broncho-gram sign, vocule sign, cavity and calciifcation. By using univariate and multivariate analysis, we found the independent predic-tors of malignancy of solid SPNs and subsequently established a clinical prediction model. hTen, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model.Results MultivariateLogistic regression analysis was used to identify eight clini-cal characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calciifcation) as independent predictors of malignancy of in solid SPNs. hTe area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and speciifcity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%.Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs.
