A model of malignant risk prediction for solitary pulmonary nodules on 18F-FDG PET/CT: building and estimating
10.3760/cma.j.issn.2095-2848.2019.03.001
- VernacularTitle:18F-FDG PET/CT孤立性肺结节恶性风险预测模型的建立及效能评价
- Author:
Yuan CHENG
1
;
Zhenguang WANG
;
Guangjie YANG
;
Simin LIU
;
Fengyu WU
;
Dacheng LI
;
Mingming YU
Author Information
1. 青岛大学附属医院PET/CT中心 266100
- Keywords:
Solitary pulmonary nodule;
Photon-emission tomography;
Tomography,X-ray computed;
Deoxyglucose;
Risk assessment
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2019;39(3):129-132
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a model of malignant risk prediction of solitary pulmonary nodules(SPN) with the metabolic characteristics of the lesion.Methods A total of 362 patients (291 malignant cases and 71 benign cases;194 males,168 females;median age:61 years) who underwent PET/CT imaging from January 2013 to July 2017 were analyzed.The diagnosis of malignant SPN was based on pathological results,and that of benign SPN was based on pathological or follow-up results.Differences of clinical/imaging characteristics in patients with benign and those with malignant SPN were analyzed.Risk factors were screened by multivariate non-conditional logistic regression analysis.The self-verification of the model was done by the receiver operating characteristic (ROC) curve analysis,out-of-group verification was performed by k-fold cross-validation.Results There were statistically significant differences in age,maximum standardized uptake value (SUVmax),size,lobulation,spiculation,pleural traction,vessel connection,calcification,vacuole,and emphysema between patients with benign and malignant nodules (all P＜0.05).The risk factors for malignant nodules included age,SUVmax,size,lobulation,calcification and vacuole.The odds ratio (OR) values (95％ CI) were 1.040(1.007-1.075),1.612(1.287-2.017),1.149(1.074-1.230),4.650(2.138-10.115),0.216(0.085-0.548),and 3.043(1.302-7.111),respectively.The logistic regression model was as follows:P=1/(1+e-x),x=-5.583+0.039×age+0.477×SUVmaxx+0.139×size+1.537×lobulation-1.532×calcification+ 1.1 13×vacuole.The estimated area under the curve (AUC) for the model was 0.915(95％ CI:0.883-0.947),sensitivity was 89.7％,specificity was 78.9％.K-fold cross-validation showed that the training accuracy was 0.899±0.011,the predictive accuracy was 0.873±0.053.Conclusions The risk factors for malignant nodules included age,SUVmax,size,lobulation,calcification and vacuole.After verification,the model has a satisfactory accuracy.It may help clinics make accurate decisions.
