Non-invasive Prediction of CYP11B2-Defined Subtypes in Primary Aldosteronism Using 18F-Pentixafor PET/CT and Machine Learning

PURPOSE: F-AlF-NOTA-Pentixafor positron emission tomography/computed tomography (PET/CT) images for the non-invasive prediction of pathological subtypes in primary aldosteronism (PA). METHODS: F-Pentixafor PET/CT between February 2024 and May 2025. Predictive models were built by integrating clinical data, PET/CT radiomics, and DL features. A two-stage feature selection strategy was employed, which utilized the minimum redundancy maximum relevance method followed by stepwise regression based on the Akaike information criterion. Four distinct models were constructed using the support vector machine algorithm, and their hyperparameters were optimized via stratified five-fold cross-validation. Model performance was rigorously evaluated by the area under the receiver operating characteristic curve (AUC), calibration analysis, and decision curve analysis. Furthermore, model interpretability was achieved using Shapley Additive Explanations (SHAP) to elucidate feature contributions. RESULTS: The combined model demonstrated superior diagnostic accuracy in the test set, with an AUC of 0.907, perfect sensitivity (1.000), and an F1-score of 0.923. It significantly outperformed the clinical, radiomics, DL models individually (p