Tomography
CT Radiomics and Machine Learning for Three-Class Bone Density Classification: An Exploratory Comparison Across Lumbar Vertebral Levels
Par Asiye Sözeri, Halitcan Batur et Nurdan Çay.
Contenu en anglais
Résumé
Objectives
The aim of this study is to provide early diagnosis of osteoporosis, osteopenia, and normal bone mineral density (BMD) on CT scans utilizing machine learning and radiomics techniques.
Methods
In this retrospective study, 874 patients who met the predefined eligibility criteria were included. BMD was categorized as normal, osteopenia, or osteoporosis according to DEXA-derived T-scores. Lumbar vertebral bodies were automatically segmented on abdominal and lumbar CT images, and radiomic features were extracted from the segmented regions. Logistic regression (LR), decision table (DT), random forest (RF), multilayer perceptron (MLP), and bagging models were developed to distinguish among the three BMD categories. Model performance was evaluated using accuracy, F-score, area under the receiver operating characteristic curve (ROC area), and area under the precision–recall curve (PRC area).