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

• Published in: Japanese journal of radiology 2024-03
• Main Authors: Nakajo, Masatoyo, Hirahara, Daisuke, Jinguji, Megumi, Ojima, Satoko, Hirahara, Mitsuho, Tani, Atsushi, Takumi, Koji, Kamimura, Kiyohisa, Ohishi, Mitsuru, Yoshiura, Takashi
• Format: Article
• Language: English
• ISSN: 1867-108X
• DOI: 10.1007/s11604-024-01546-y

To investigate the usefulness of machine learning (ML) models using pretreatment F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS). This retrospective study included 47 patients with CS who underwent F-FDG-PET/CT scan before treatment. The lesions were assigned to the training (n = 38) and testing (n = 9) cohorts. In total, 49 F-FDG-PET-based radiomic features and the visibility of right ventricle 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. The ML models were constructed using the top four features ranked by the decrease in Gini impurity. The AUCs and accuracies were used to compare predictive performances. Patients who developed ACEs presented with a significantly higher surface area and gray level run length matrix run length non-uniformity (GLRLM_RLNU), and lower neighborhood gray-tone difference matrix_coarseness and sphericity than those without ACEs (each, p  0.80 (range: 0.841-0.944). In the testing cohort, the RF algorithm had the highest AUC and accuracy (88.9% [8/9]) with a similar classification performance between training and testing cohorts (AUC: 0.945 vs 0.889). GLRLM_RLNU was the most important feature of the modeling process of this RF algorithm. ML analyses using F-FDG-PET-based radiomic features may be useful for predicting ACEs in patients with CS.