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Radiomics models based on <jats:sup>18<\/jats:sup>F\u2010FDG PET\/CT have great potential in identifying EGFR mutation status and EGFR subtypes in patients with LUAD. The validation of multi\u2010center data, model visualization, and interpretation are significantly important for the management, application and trust of machine learning predictive models. However, few EGFR\u2010related research involved model visualization and interpretation, and multi\u2010center trial.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To develop explainable optimal predictive models based on handcrafted radiomics features (HRFs) extracted from multi\u2010center <jats:sup>18<\/jats:sup>F\u2010FDG PET\/CT to predict EGFR mutation status and molecular subtypes in LUAD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Baseline <jats:sup>18<\/jats:sup>F\u2010FDG 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\u2010combination 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>STACK feature selection method combined with light gradient boosting machine (LGBM) reached optimal performance in identifying EGFR mutation status ([area under the curve] AUC\u00a0=\u00a00.81 in the internal test cohort; AUC\u00a0=\u00a00.62 in the external test cohort). Random forest feature selection method combined with LGBM reached optimal performance in predicting EGFR mutation molecular subtypes (AUC\u00a0=\u00a00.89 in the internal test cohort; AUC\u00a0=\u00a00.61 in the external test cohort).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Explainable machine learning models combined with radiomics features extracted from multi\u2010center\/scanner <jats:sup>18<\/jats:sup>F\u2010FDG PET\/CT have certain potential to identify EGFR mutation status and subtypes in LUAD, which might be helpful to the treatment of LUAD.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16947","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T18:13:34Z","timestamp":1706552014000},"page":"4872-4887","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Value of multi\u2010center <sup>18<\/sup>F\u2010FDG PET\/CT radiomics in predicting EGFR 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