[Correlations of 18F-FDG PET/CT Metabolic Parameters and Metabolic Heterogeneity with Human Epidermal Growth Factor Receptor 2 Expression in Patients with Gastric Cancer].

Objective To investigate the value of 18F-FDG PET/CT metabolic parameters and metabolic heterogeneity for predicting the expression of human epidermal growth factor receptor 2 (HER2) in patients with gastric cancer. Methods A total of 45 patients with gastric cancer confirmed by surgical pathology between September 2016 and May 2021 were enrolled in this study.All the patients underwent 18F-FDG PET/CT examination before surgery.The maximum standardized uptake value (SUVmax),metabolic tumor volume (MTV),and total lesion glycolysis (TLG) of primary gastric cancer were measured,and the linear regression slope of MTV corresponding to different SUVmax thresholds (40% SUVmax and 80% SUVmax) was calculated.The absolute value of the slope was deemed to represent the metabolic heterogeneity of primary gastric cancer,termed the heterogeneity index (HI).Univariate and multivariate Logistic regression analyses were conducted to evaluate the correlations of 18F-FDG PET/CT metabolic parameters and HI with HER2 expression. Results The 45 patients included 10 with positive HER2 expression and 35 with negative result.The MTV (P=0.043) and HI (P=0.048) were lower in the patients with positive HER2 expression than in the patients with negative HER2 expression.The MTV and HI had the optimal thresholds of 12.10 cm3 and 3.71,respectively,which respectively showed the accuracy of 62.2% and 57.8% for predicting HER2 expression.The univariate Logistic regression showed that the tumor differentiation degree,MTV,and HI were correlated with HER2 expression,while the multivariate Logistic regression showed that only the tumor differentiation degree (OR=20.130,95%CI=1.843-219.860,P=0.014) was an independent predictor for HER2 expression.A further stratified analysis of the tumor differentiation degree showed that HER2 expression only varied among different MTV threshold groups in patients with moderately/well differentiated gastric cancer (P=0.031). Conclusions MTV and HI were associated with HER2 expression in gastric cancer,whereas neither played an independent predictive role.Therefore,these factors should be combined with clinicopathological characteristics of patients to jointly guide treatment decisions.

Predictive value of multiple metabolic and heterogeneity parameters of 18F-FDG PET/CT for EGFR mutations in non-small cell lung cancer.

OBJECTIVES: To explore the value of multiple metabolic and heterogeneity parameters of 2-deoxy-2-[fluorine-18] fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) in predicting epidermal growth factor receptor gene (EGFR) mutations in non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: A retrospective analysis was performed by reviewing 98 patients with NSCLC who underwent EGFR mutation testing and 18F-FDG PET/CT examination in our hospital between March 2016 and March 2021. Patients were divided into an EGFR-mutant group and a wild-type group. A multivariate logistic regression analysis was performed to screen and construct a prediction model. The diagnostic performance of the model was evaluated using a receiver-operating characteristic (ROC) curve. RESULTS: The study found that EGFR mutations were more likely to occur in women, non-smokers, and patients with peripheral lesions, shorter maximum tumor diameter, adenocarcinoma, and T1 stage cancer. Low maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, total lesion glycolysis, and high coefficient of variation (COV) were significantly correlated with EGFR mutations, and the area under the ROC curve (AUC) was 0.622, 0.638, 0.679, 0.687, and 0.672, respectively. Multivariate logistic regression analysis indicated that non-smokers (odds ratio (OR) = 0.109, P = 0.014), peripheral lesions (OR = 6.917, P = 0.022), low SUVmax (≤ 7.85, OR = 5.471, P = 0.001), SUVmean (≤ 5.34, OR = 0.044, P = 0.000), and high COV (≥ 106.08, OR = 0.996, P = 0.045) were independent predictors of EGFR mutations. The AUC of the prediction model established by combining the above factors was 0.926; the diagnostic efficiency was significantly higher than that of a single parameter. CONCLUSION: Among the metabolic and heterogeneity parameters of 18F-FDG PET/CT, low SUVmax, SUVmean, and high COV were significantly associated with EGFR mutations, and the predictive value of EGFR mutations could be enhanced when combined with clinicopathological features.