Can radiomics signatures and machine learning methods reinforce the revived role of 18F-NaF in metastatic bone disease?

Objectives: F-NaF PET/CT. Methods: F-NaF PET/CT. Lesions were classified as TP or FP based on consensus interpretation including follow-up. Patients were randomly split into training (n=41) and validation (n=21) groups. Radiomic features were extracted from PET images using LIFEx software. Feature selection (ANOVA, RFE) and ML model training (SVM, Random Forest, XGBoost) were performed. Model performance was evaluated using accuracy, specificity, sensitivity, and AUC, initially with a train/validation split and subsequently with 5-fold cross-validation incorporating feature engineering and hyperparameter tuning. Feature importance was assessed using SHAP. Results: , Voxel Volume Num, GLRLM RLNU, and Skew. Conclusion: F-NaF PET/CT for assessing bone metastases. Further validation in larger cohorts is warranted.