Verification, comparison and melioration of different prediction models for solitary pulmonary nodule
10.7507/1007-4848.201711079
- VernacularTitle:对孤立性肺结节恶性概率预测模型的验证、比较和改良
- Author:
BAO Tong
1
;
XIAO Fei
1
;
GUO Yongqing
1
;
SHI Bin
1
;
SONG Zhiyi
1
;
LIANG Chaoyang
1
;
SUN Hongliang
2
;
LIU Deruo
1
Author Information
1. Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, 100029, P.R.China
2. Department of Radiology, China-Japan Friendship Hospital, Beijing, 100029, P.R.China
- Publication Type:Journal Article
- Keywords:
Solitary pulmonary nodule;
lung cancer;
prediction model
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2018;25(6):471-476
- CountryChina
- Language:Chinese
-
Abstract:
Objective To identify risk factors that affect the verification of malignancy in patients with solitary pulmonary nodule (SPN) and verify different prediction models for malignant probability of SPN. Methods We retrospectively analyzed the clinical data of 117 SPN patients with definite postoperative pathological diagnosis who underwent surgical procedure in China-Japan Friendship Hospital from March to September 2017. There were 59 males and 58 females aged 59.10±11.31 years ranging from 24 to 83 years. Imaging features of the nodule including maximum diameter, location, spiculation, lobulation, calcification and serum level of CEA and Cyfra21-1 were assessed as potential risk factors. Univariate analysis was used to establish statistical correlation between risk factors and postoperative pathological diagnosis. Receiver operating characteristic (ROC) curve was drawn by different predictive models for the malignant probability of SPN to get areas under the curves (AUC), sensitivity, specificity, positive predictive values, negative predictive values for each model. The predictive effectiveness of each model was statistically assessed subsequently. Results Among 117 patients, 93 (79.5%) were malignant and 24 (20.5%) were benign. Statistical difference was found between the benign and malignant group in age, maximum diameter, serum level of CEA and Cyfra21-1, spiculation, lobulation and calcification of the nodules. The AUC value was 0.813±0.051 (Mayo model), 0.697±0.066 (VA model) and 0.854±0.045 (Peking University People's Hospital model), respectively. Conclusion Age, maximum diameter of the nodule, serum level of CEA and Cyfra21-1, spiculation, lobulation and calcification are potential independent risk factors associated with the malignant probability of SPN. Peking University People's Hospital model is of high accuracy and clinical value for patients with SPN. Adding serum index into the prediction model as a new risk factor and adjusting the weight of age in the model may improve the accuracy of prediction for SPN.
