Masatoyo Nakajo, Daisuke Hirahara, Megumi Jinguji, Satoko Ojima, Mitsuho Hirahara, Atsushi Tani, Koji Takumi, Kiyohisa Kamimura, Mitsuru Ohishi, Takashi Yoshiura
OBJECTIVES: To investigate the usefulness of machine learning (ML) models using pretreatment 18 F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS). MATERIALS AND METHODS: This retrospective study included 47 patients with CS who underwent 18 F-FDG-PET/CT scan before treatment. The lesions were assigned to the training (n = 38) and testing (n = 9) cohorts. In total, 49 18 F-FDG-PET-based radiomic features and the visibility of right ventricle 18 F-FDG uptake were used to predict ACEs using seven different ML algorithms (namely, decision tree, random forest [RF], neural network, k-nearest neighbors, Naïve Bayes, logistic regression, and support vector machine [SVM]) with tenfold cross-validation and the synthetic minority over-sampling technique...
March 16, 2024: Japanese Journal of Radiology