Machine learning approach using ¹⁸F-FDG-PET-radiomic features and the visibility of right ventricle ¹⁸F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis.

ABSTRACT:

Objectives

To investigate the usefulness of machine learning (ML) models using pretreatment ¹⁸F-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 ¹⁸F-FDG-PET/CT scan before treatment. The lesions were assigned to the training (n = 38) and testing (n = 9) cohorts. In total, 49 ¹⁸F-FDG-PET-based radiomic features and the visibility of right ventricle ¹⁸F-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]) wit

SUBMITTER: Nakajo M 

PROVIDER: S-EPMC11217075 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature