Opportunistic Prediction of Osteoporosis with Machine Learning Models Based on Clivus-radiomic Features Obtained from CT Images

Abstract

Objective: Osteoporosis (OP) is a major public health problem that causes significant mortality and morbidity. Therefore, early diagnosis is essential. We aimed to predict OP by combining computed tomography (CT)-based radiomic data of the clivus with machine learning (ML) algorithms. Materials and Methods: In this retrospective study, 140 cases that underwent dual energy X-ray absorptiometry (DEXA) and craniofacial CT within one year of each other between 2015 and 2021, were examined at our institution. According to DEXA T-scores, cases were divided into three groups: 30 OP, 33 osteopenia, and 77 normal. Trabecular components of the clivus were segmented, and 1023 radiomic features were extracted using 3D Slicer. Radiomic outputs consist of features from original, Laplacian of Gaussian, and wavelet transform filtered images. Voxel resampling was standardized as 1x1x1 mm³. Orange Data Mining program was used for ML. Relief and fast correlation-based filter were used for feature reduction. K-nearest neighborhood, decision tree, random forest, logistic regression, support vector machine (SVM), Naive Bayes, and neural network were used as classifiers. Area under the curve (AUC), sensitivity, specificity, receiver operating characteristic curve, and confusion matrix were used for performance evaluation. Results: In binary classification as OP and non-OP, neural network achieved the highest success in predicting OP (AUC 0.87). In the binary classification of BMD as low BMD and normal BMD, SVM was the best in predicting low BMD cases (AUC: 0.82). In the ternary classification of BMD as OP, osteopenia, and normal, Naive Bayes achieved the highest performance in distinguishing OP (AUC: 0.9) and osteopenia (AUC: 0.69). The Hounsfield Units values of the clivus were significantly different between low BMD and normal BMD cases (p<0.001). Conclusion: ML algorithms using CT-based radiomic features of the clivus can predict OP and provide BMD information.

Keywords

• osteoporosis
• clivus
• machine learning
• radiomics