The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations

作者：Xieraili Wumener, Yarong Zhang, Zihan Zang, Fen Du, Xiaoxing Ye, Maoqun Zhang, Ming Liu, Jiuhui Zhao, Tao Sun, Ying Liang · 发表于：BMC Pulmonary Medicine · 年份：2024 · DOI：10.1186/s12890-024-02997-9 · 被引用次数：15 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Medical Imaging Techniques and Applications、Lung Cancer Diagnosis and Treatment

Abstract Objectives 18 F-fluorodeoxyglucose (FDG) PET/CT has been widely used for the differential diagnosis of cancer. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may make it difficult to differentiate between benign and malignant lesions. It is crucial to find reliable quantitative metabolic parameters to further support the diagnosis. This study aims to evaluate the value of the quantitative metabolic parameters derived from dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status. Methods We included 147 patients with lung lesions to perform FDG PET/CT dynamic plus static imaging with informed consent. Based on the results of the postoperative pathology, the patients were divided into benign/malignant groups, adenocarcinoma (AC)/squamous carcinoma (SCC) groups, and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Quantitative parameters including K 1 , k 2 , k 3 , and K i of each lesion were obtained by applying the irreversible two-tissue compartmental modeling using an in-house Matlab software. The SUV analysis was performed based on conventional static scan data. Differences in each metabolic parameter among the group were analyzed. Wilcoxon rank-sum test, independent-samples T-test, and receiver-operating characteristic (ROC) analysis were performed to compare the diagnostic effects among the differentiated groups. P < 0.05 were con...