Predictive value of 18F-FDG PET/CT radiomics for EGFR mutation status in non-small cell lung cancer: a systematic review and meta-analysis.

ABSTRACT

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, Cochrane Library, Web of Science, and CNKI 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 were 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. Within the training cohort, the application of 18F-FDG PET/CT radiomics in predicting EGFR mutations in NSCLC demonstrated a sensitivity of 0.76 (95% CI 0.70-0.81) and a specificity of 0.78 (95% CI 0.74-0.82), accompanied by a positive likelihood ratio of 3.5 (95% CI3.0-4.2), a negative likelihood ratio of 0.31 (95% CI 0.24-0.39), a diagnostic odds ratio of 11.0 (95% CI 8.0-16.0), and an area under the curve (AUC) of 0.84 (95% CI 0.80-0.87). In the validation cohort, the values included a sensitivity of 0.76 (95% CI 0.67-0.83), a specificity of 0.75 (95% CI 0.68-0.80), a positive likelihood ratio of 3.0 (95% CI2.4-3.8), a negative likelihood ratio of 0.32 (95% CI 0.24-0.44), a 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. Systematic review registration https//www.crd.york.ac.uk/PROSPERO, identifier CRD42022385364.