Establishment and validation of a nomogram model for predicting EGFR mutations in lung adenocarcinoma
10.3760/cma.j.cn321828-20210317-00074
- VernacularTitle:预测肺腺癌EGFR突变的列线图模型的建立及验证
- Author:
Hongyue ZHAO
1
;
Yexin SU
;
Mengjiao WANG
;
Peng FU
Author Information
1. 哈尔滨医科大学附属第一医院核医学科，哈尔滨 150001
- Keywords:
Lung neoplasms;
Adenocarcinoma;
Genes, erbB-1;
Mutation;
Nomograms;
Forecasting
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2022;42(10):577-582
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct and validate a nomogram model based on clinical factors and PET/CT metabolic parameters of 18F-FDG for predicting epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma. Methods:From January 2014 to January 2019, 114 patients (59 males, 55 females, age (60.0±10.8) years) with lung adenocarcinoma in the First Affiliated Hospital of Harbin Medical University were retrospectively enrolled. Clinical data (smoking status, tumor location, clinical stage and carcinoembryonic antigen (CEA) level), 18F-FDG PET/CT metabolic parameters (SUV max, metabolic tumor volume (MTV) and total lesion glycolysis (TLG)) and EGFR mutation status were analyzed. Patients were divided into training group (80 cases) and validation group (34 cases). In the training group, univariate analyses (independent-sample t test, Wilcoxon rank sum test, χ2 test or Fisher′s exact probability method) were used for categorical variables. Variables that showed significant differences between EGFR mutation group and wild type group were selected. Variance inflation factors (VIF) were calculated and the collinearity variables were deleted, and a nomogram model of optimal logistic model was constructed based on Akaike information criterion (AIC). The effect of the model was evaluated by the concordance index (C-index), sensitivity, specificity, accuracy, calibration and decision curve analysis (DCA) in the training group and the validation group. Results:Among 114 patients, 56 were with EGFR mutations and 58 were with EGFR wild type. In the training group, there were significant differences in gender (male/female: 14/26 vs 25/15; χ2=6.05, P=0.014), smoking status (with/without smoking history: 4/36 vs 22/18; χ2=18.46, P<0.001) and SUV max (5.72(3.90, 8.32) vs 8.09(4.56, 12.55); W=1 045.50, P=0.018) between EGFR mutation group and wild type group. However, there were no significant differences in other factors ( t=-0.54, χ2 values: 0.20 and 0.20, W values: 921.50 and 983.00, all P>0.05). The VIF of gender, smoking status and SUV max were all less than 10, and the nomogram model with three factors showed the minimum AIC (90.06). In the training group, C-index value of the model was 0.798 (95% CI: 0.699-0.897), with the sensitivity of 85.0%(34/40), the specificity of 70.0%(28/40) and the accuracy of 77.5%(62/80). In the validation group, C-index value was 0.854(95% CI: 0.725-0.984), with the sensitivity of 13/16, the specificity of 14/18, and the accuracy of 79.4%(27/34). The calibration curve and the goodness of fit test showed good calibration, and DCA showed that the model could benefit patients clinically within a large risk threshold range (training group: 0-0.59, validation group: 0-0.65). Conclusion:The nomogram model based on gender, smoking status and SUV max can be used to easily predict EGFR mutation status in lung adenocarcinoma.
