Predictive value of intratumoral heterogeneity measured by 18F-FDG PET/CT for EGFR mutation of adenocarcinoma
10.3760/cma.j.cn321828-20220824-00269
- VernacularTitle:18F－FDG PET/CT肿瘤内代谢异质性参数预测肺腺癌EGFR基因突变的价值
- Author:
Nan CHENG
1
;
Guqing ZHANG
;
Ming GAO
;
Xun WANG
;
Yu KONG
Author Information
1. 济宁医学院附属医院医学影像科，济宁 272000
- Keywords:
Lung neoplasms;
Adenocarcinoma;
Mutation;
Receptor, epidermal growth factor;
Positron-emission tomography;
Tomography, X-ray computed;
Fluorodeoxyglucose F1
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2024;44(1):1-5
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of traditional metabolic parameters, CT features and intratumoral heterogeneity parameters measured by 18F-FDG PET/CT in predicting the mutation status of the epidermal growth factor receptor (EGFR) gene in patients with adenocarcinoma. Methods:A total of 147 patients (73 males, 74 females, age (59.8±10.2) years) with pathological confirmed adenocarcinoma between January 2016 and June 2020 in the Affiliated Hospital of Jining Medical University were retrospectively included. The differences of clinical data (smoking history, tumor location and clinical stage), CT features (maximum diameter, ground-glass opacity content, lobulation, speculation, cavitation, air-bronchogram, pleural retraction and bronchial cut-off sign), 18F-FDG PET/CT traditional metabolic parameters (SUV max, SUV mean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG)) and intratumoral heterogeneity parameters ( CV, heterogeneity index (HI)) were analyzed between patients with EGFR mutation and patients with EGFR wild-type. Independent-sample t test, Mann-Whitney U test and χ2 test were used to analyze the data. Multivariate logistic regression was used to analyze the predictors of EGFR mutation. ROC curve analysis was used to evaluate the predictive value of clinical and PET/CT information. Results:Among 147 patients, 87 were with EGFR mutation and 60 were with EGFR wild-type. There were significant differences in gender (male/female), smoking history (with/without), location (peripheral lesion/central lesion), pleural retraction (with/without), SUV max, SUV mean, TLG, CV and HI ( χ2 values: 4.72-23.89, z values: from -2.31 to 5.74, all P<0.05). Multivariate logistic regression analysis showed that smoking history (odds ratio ( OR)=0.167, 95% CI: 0.076-0.366; P<0.001), pleural retraction ( OR=1.404, 95% CI: 1.115-3.745; P=0.012), SUV max ( OR=0.922, 95% CI: 0.855-0.995; P=0.003), TLG ( OR=0.991, 95% CI: 0.986-0.996; P=0.001) and HI ( OR=0.796, 95% CI: 0.700-0.859; P<0.001) were predictors of EGFR mutation. ROC curve analysis showed the AUC of HI was 0.779, with the sensitivity of 76.67%(46/60) and the specificity of 79.31%(69/87). The predictive model was constructed by combining smoking history, pleural retraction, TLG, SUV max and HI, and the AUC was 0.908, with the sensitivity of 88.33%(53/60) and the specificity of 68.97%(60/87). The difference of AUCs between HI and the predictive model was statistically significant ( z=3.71, P<0.001). Conclusion:HI can predict EGFR mutations better, and the predictive value for EGFR mutations can be enhanced when combining HI with smoking history, pleural retraction, TLG and SUV max.
