Dynamic FDG PET/CTO enhances lung cancer subtype differentiation and EGFR mutation status prediction

Physician's Weekly - 16/05/2024 

The widespread utilisation of  ¹⁸F-fluorodeoxyglucose (FDG) PET/CT for cancer differential diagnosis necessitates a robust understanding of its limitations, particularly regarding semi-quantitative standardised 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 categorisation into benign/malignant, adenocarcinoma (AC)/squamous carcinoma (SCC), and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Utilising irreversible 2-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), SUV_max , k₂, k₃, and K_i exhibited significantly higher values compared with the benign group (n=23), demonstrating superior discriminatory performance (P<0.05). Similarly, in the AC group (n=88), SUV_max, k₃, and Ki were significantly lower than in the SCC group, yielding statistically significant distinctions (P<0.05). Notably, ROC analysis revealed K_i to possess superior diagnostic specificity compared with SUV_max, 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), K_i demonstrated a significant difference between EGFR (+) and EGFR (-) groups (P<0.05), outperforming SUV_max and k₃ in this regard.

Quantitative metabolic parameters derived from dynamic FDG PET/CT, particularly K_i, 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

Originally Published By Physician’s Weekly. Reused by Medicom Medical Publishers with permission.

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