Value of multi‐center 18F‐FDG PET/CT radiomics in predicting EGFR mutation status in lung adenocarcinoma

Abstract Background Accurate, noninvasive, and reliable assessment of epidermal growth factor receptor (EGFR) mutation status and EGFR molecular subtypes is essential for treatment plan selection and individualized therapy in lung adenocarcinoma (LUAD). Radiomics models based on 18 F‐FDG PET/CT have great potential in identifying EGFR mutation status and EGFR subtypes in patients with LUAD. The validation of multi‐center data, model visualization, and interpretation are significantly important for the management, application and trust of machine learning predictive models. However, few EGFR‐related research involved model visualization and interpretation, and multi‐center trial. Purpose To develop explainable optimal predictive models based on handcrafted radiomics features (HRFs) extracted from multi‐center 18 F‐FDG PET/CT to predict EGFR mutation status and molecular subtypes in LUAD. Methods Baseline 18 F‐FDG PET/CT images of 383 LUAD patients from three hospitals and one public data set were collected. Further, 1808 HRFs were extracted from the primary tumor regions using Pyradiomics. Predictive models were built based on cross‐combination of seven feature selection methods and seven machine learning algorithms. Yellowbrick and explainable artificial intelligence technology were used for model visualization and interpretation. Receiver operating characteristic curve, classification report and confusion matrix were used for model performance evaluation. Clinical applicability of the optimal models was assessed by decision curve analysis. Results STACK feature selection method combined with light gradient boosting machine (LGBM) reached optimal performance in identifying EGFR mutation status ([area under the curve] AUC = 0.81 in the internal test cohort; AUC = 0.62 in the external test cohort). Random forest feature selection method combined with LGBM reached optimal performance in predicting EGFR mutation molecular subtypes (AUC = 0.89 in the internal test cohort; AUC = 0.61 in the external test cohort). Conclusions Explainable machine learning models combined with radiomics features extracted from multi‐center/scanner 18 F‐FDG PET/CT have certain potential to identify EGFR mutation status and subtypes in LUAD, which might be helpful to the treatment of LUAD.