Acad Radiol. 2024 Oct 28:S1076-6332(24)00780-3. doi: 10.1016/j.acra.2024.10.019. Online ahead of print.
ABSTRACT
OBJECTIVE: We investigated the value of PET/CT-based multimodal parameters in predicting the degree of differentiation and epidermal growth factor receptor (EGFR) mutations in invasive lung adenocarcinoma (ILA) and assessed the correlation between PET/CT-based multimodal parameters and Ki67.
METHODS: We retrospectively collected 113 patients with ILA who underwent PET/CT examination, and differences in PET/CT multimodal parameters between different differentiation groups were analyzed. Binary logistic regression was used to establish a multiparameter model for predicting EGFR mutation, and ROC curve was used to compare the diagnostic efficiency. Independent predictors of the Ki67 index were screened using multiple linear regression analysis.
RESULTS: The poorly differentiated group was more likely to have large-diameter, solid foci, pleural pulling signs, and vacuolar signs compared with other groups (all P < 0.05). The differences in metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in all three different differentiated groups were statistically significant compared to the other parameters (all P < 0.05). The PET/CT regression model predicted EGFR mutations with an AUC of 0.820 and was higher than other models; the sensitivity, specificity, positive predictive value, and negative predictive value for discriminating EGFR mutations were 84.74%, 70.37%, 75.76%, and 80.85%, respectively. PET/CT multiple linear regression analysis showed that vascular convergence, SUVpeak, MTV, and TLG explaining 62.0% changes in Ki67 (R2 = 0.620). SUVpeak, MTV, and TLG (r = 0.580, r = 0.662, and r = 0.680, all P < 0.001) were all strongly correlated with increased Ki67 index.
CONCLUSION: MTV and TLG can better identify the degree of ILA differentiation compared to CT and other PET parameters. The nomogram constructed by multimodal PET/CT parameters can better dynamically monitor the changes of EGFR status and Ki67 index.
PMID:39472205 | DOI:10.1016/j.acra.2024.10.019
