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The following is a summary of “Value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations,” published in the May 2024 issue of Pulmonology by Wumener et al.
The widespread utilization of 18F-fluorodeoxyglucose (FDG) PET/CT for cancer differential diagnosis necessitates a robust understanding of its limitations, particularly regarding semi-quantitative standardized uptake value (SUV) analysis, which may lack the specificity required to differentiate benign and malignant lesions effectively. To address this, the study aims to assess the diagnostic efficacy of quantitative metabolic parameters derived from dynamic FDG PET/CT in distinguishing lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status.
A cohort of 147 patients with lung lesions underwent FDG PET/CT, comprising both dynamic and static imaging modalities, following informed consent. Postoperative pathology results facilitated patient categorization into benign/malignant, adenocarcinoma (AC)/squamous carcinoma (SCC), and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Utilizing irreversible two-tissue compartmental modeling and in-house Matlab software, quantitative parameters including K1, k2, k3, and Ki were derived for each lesion. Additionally, SUV analysis based on conventional static scan data was performed. Statistical analyses, including the Wilcoxon rank-sum test, independent-sample T-test, and receiver-operating characteristic (ROC) analysis, were conducted to discern differences in metabolic parameters among groups and compare diagnostic efficacy.
Within the malignant group (N = 124), SUVmax , k2, k3, and Ki exhibited significantly higher values compared to the benign group (N = 23), demonstrating superior discriminatory performance (P < 0.05). Similarly, in the AC group (N = 88), SUVmax, k3, and Ki were significantly lower than in the SCC group, yielding statistically significant distinctions (P < 0.05). Notably, ROC analysis revealed Ki to possess superior diagnostic specificity compared to SUVmax, particularly evident with a cut-off value of 0.0250 ml/g/min (AUC = 0.999 vs. 0.70). Moreover, among patients undergoing EGFR testing (N = 48), Ki demonstrated a significant difference between EGFR (+) and EGFR (-) groups (P < 0.05), outperforming SUVmax and k3 in this regard.
Quantitative metabolic parameters derived from dynamic FDG PET/CT, particularly Ki, exhibit enhanced diagnostic specificity compared to SUVmax, notably in differentiating lung cancer subtypes and predicting EGFR mutation status. The findings underscore the potential of dynamic imaging as a valuable non-invasive screening tool, particularly in settings where EGFR testing is unavailable.
Source: bmcpulmmed.biomedcentral.com/articles/10.1186/s12890-024-02997-9