A nomogram based on 18F-fluorodeoxyglucose PET/CT and clinical features to predict epidermal growth factor receptor mutation status in patients with lung adenocarcinoma

Yue Guo; Hui Zhu; Congxia Chen; Xu Li; Fugeng Liu; Zhiming Yao

doi:10.21037/qims-22-248

A nomogram based on ¹⁸F-fluorodeoxyglucose PET/CT and clinical features to predict epidermal growth factor receptor mutation status in patients with lung adenocarcinoma

Quant Imaging Med Surg. 2022 Nov;12(11):5239-5250. doi: 10.21037/qims-22-248.

Authors

Yue Guo¹, Hui Zhu¹, Congxia Chen¹, Xu Li¹, Fugeng Liu¹, Zhiming Yao¹

Affiliation

¹ Department of Nuclear Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.

Abstract

Background: Identifying epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma (LADC) is vital for treatment decision-making. This study aimed to establish a convenient and noninvasive nomogram prediction model based on ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) imaging and clinical features to predict EGFR mutation status in patients with LADC.

Methods: A total of 274 patients (male 130, female 144, median age 65 years) were enrolled in this retrospective study. Imaging data from ¹⁸F-FDG PET/CT and clinical information were analyzed, with the Mann-Whitney U test, Student's t-test, and chi-square test used to compare categorical or continuous covariates as appropriate. Logistic regression analyses were performed to identify independent variables associated with EGFR mutation status, from which the nomogram prediction model was constructed. Leave-one-out cross-validation was performed, and the discrimination ability and calibration of the nomogram were assessed by calculating the area under the curve of the receiver operating characteristic curve and the calibration curve. The clinical net benefit of the nomogram was evaluated.

Results: Of the 274 patients, 143 (52.2%) had EGFR mutations. Female sex [odds ratios (OR): 2.64, 95% confidence interval (CI): 1.29-5.45, P=0.008], non-smoking status (OR: 2.78, 95% CI: 1.30-5.88, P=0.008), mean standardized uptake value ≤9.23 (OR: 2.44, 95% CI: 1.35-4.55, P=0.004), metabolic tumor volume ≤17.72 cm³ (OR: 5.00, 95% CI: 2.38-12.50, P<0.001) and the presence of pleural retraction (OR: 1.88, 95% CI: 1.05-3.40, P=0.034) were independent predictors for EGFR mutations in LADCs. The nomogram based on these risk factors showed good predictive efficacy, with an area under the curve of 0.805 (95% CI: 0.753-0.857), a sensitivity of 90.2%, a specificity of 59.5% and an accuracy of 73.0%.

Conclusions: The nomogram prediction model incorporating sex, smoking status, mean standardized uptake value, metabolic tumor volume, and the presence of pleural retraction could effectively discriminate EGFR-mutant from wild-type LADCs.

Keywords: 18F-fluorodeoxyglucose positron emission tomography-computed tomography; epidermal growth factor receptor mutation; lung adenocarcinoma (LADC); nomogram.