Predictive value of 18F-FDG PET/CT radiomics for EGFR mutation status in non-small cell lung cancer: a systematic review and meta-analysis - Ma, Ning; Yang, Weihua; Wang, Qiannan; Cui, Caozhe; Hu, Yiyi; Wu, Zhifang (Frontiers in Oncology, 2024)
Objective: This study aimed to evaluate the value of 18F-FDG PET/CT radiomics in predicting EGFR gene mutations in non-small cell lung cancer by meta-analysis. Methods: The PubMed, EMBASE, Web of Science and Cochrane library databases were searched from the earliest available date to June 30, 2023. The meta-analysis was performed using the Stata 15.0 software. The methodological quality and risk of bias of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score criteria. The possible causes of heterogeneity were analyzed by meta-regression. Results: A total of 17 studies involving 3763 non-small cell lung cancer patients were finally included. We analyzed 17 training cohorts and 10 validation cohorts independently. In the training cohort, 18F-FDG PET/CT radiomics for predicting EGFR mutations in NSCLC yielded a sensitivity of 0.76 (95% CI: 0.70-0.81), specificity of 0.78 (95% CI: 0.74-0.82), positive likelihood ratio of 3.5 (95% CI: 3.0-4.2), negative likelihood ratio of 0.31 (95% CI: 0.24-0.39), diagnostic odds ratio of 11.0 (95% CI: 8.0-16.0), and an AUC of 0.84 (95% CI: 0.80-0.87). In the validation cohort, the values were sensitivity of 0.76 (95% CI: 0.67-0.83), specificity of 0.75 (95% CI: 0.68-0.80), positive likelihood ratio of 3.0 (95% CI: 2.4-3.8), negative likelihood ratio of 0.32 (95% CI: 0.24-0.44), diagnostic odds ratio of 9 (95% CI: 6-15), and an AUC of 0.82 (95% CI: 0.78-0.85). The average Radiomics Quality Score (RQS) across studies was 10.47 ± 4.72. Meta-regression analysis identifies the application of deep learning and regions as sources of heterogeneity. Conclusion: 18F-FDG PET/CT radiomics may be useful in predicting mutation status of the EGFR gene in non-small cell lung cancer.