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<p>Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), is associated with poor prognosis. This study investigates the prediction of EGFR mutation status in LUAD using 18F-FDG PET/CT imaging (SUVmax) and clinical factors to guide targeted therapy. A retrospective analysis was conducted on 409 LUAD patients who underwent preoperative 18F-FDG PET/CT and EGFR mutation testing between July 2019 and December 2022. Patients were divided into EGFR mutation-positive (EGFR-MT, <i>n</i> = 283) and wild-type (EGFR-WT, <i>n</i> = 126) groups. Univariate and multivariate logistic regression analyses were performed to identify EGFR mutation predictors. EGFR-MT was present in 69.2% of patients. Female sex, non-smokers, well-differentiated tumors, and lower SUVmax were significantly associated with EGFR mutations. Multivariate analysis revealed female sex (OR = 0.44), well-differentiated tumors (OR = 0.25), and SUVmax &lt; 9.2 (OR = 0.49) as independent predictors. ROC analysis showed moderate predictive accuracy with a combined model (AUC = 0.681). EGFR mutation status in LUAD correlates with SUVmax, sex, and tumor differentiation. These factors, especially when tissue is insufficient for genetic testing, may help predict EGFR mutations and guide personalized therapy.</p>

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Predicting EGFR Mutation Status in Lung Adenocarcinoma using 18F-FDG PET/CT Maximum Standardized Uptake Value and Clinical Features

  • 期刊论文

  • 发表日期：

    2025年12月18日

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  • Mengyang Li,
  • Zhongxin Yang

摘要

Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), is associated with poor prognosis. This study investigates the prediction of EGFR mutation status in LUAD using 18F-FDG PET/CT imaging (SUVmax) and clinical factors to guide targeted therapy. A retrospective analysis was conducted on 409 LUAD patients who underwent preoperative 18F-FDG PET/CT and EGFR mutation testing between July 2019 and December 2022. Patients were divided into EGFR mutation-positive (EGFR-MT, n = 283) and wild-type (EGFR-WT, n = 126) groups. Univariate and multivariate logistic regression analyses were performed to identify EGFR mutation predictors. EGFR-MT was present in 69.2% of patients. Female sex, non-smokers, well-differentiated tumors, and lower SUVmax were significantly associated with EGFR mutations. Multivariate analysis revealed female sex (OR = 0.44), well-differentiated tumors (OR = 0.25), and SUVmax < 9.2 (OR = 0.49) as independent predictors. ROC analysis showed moderate predictive accuracy with a combined model (AUC = 0.681). EGFR mutation status in LUAD correlates with SUVmax, sex, and tumor differentiation. These factors, especially when tissue is insufficient for genetic testing, may help predict EGFR mutations and guide personalized therapy.