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.
Jpn J Radiol
; 42(7): 744-752, 2024 Jul.
Article
en En
| MEDLINE
| ID: mdl-38491333
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 ANDMETHODS:
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]) 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.RESULTS:
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 < 0.05). In the training cohort, all seven ML algorithms had a good classification performance with AUC values of > 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.CONCLUSION:
ML analyses using 18F-FDG-PET-based radiomic features may be useful for predicting ACEs in patients with CS.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Sarcoidosis
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Radiofármacos
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Fluorodesoxiglucosa F18
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Aprendizaje Automático
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Tomografía Computarizada por Tomografía de Emisión de Positrones
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Ventrículos Cardíacos
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Cardiomiopatías
Límite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Jpn J Radiol
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
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RADIOLOGIA
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RADIOTERAPIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Japón