Objective
This study aimed to develop a predictive model to detect osteoporosis using radiomic features from lumbar spine computed tomography (CT) images.
Methods
A total of 133 patients were included in this retrospective study, 41 men and 92 women, with a mean age of 65.45 ± 9.82 years (range: 31–94 years); 53 had normal bone mineral density, 32 osteopenia, and 48 osteoporosis. For each patient, the L1–L4 vertebrae on the CT images were automatically segmented using SenseCare and defined as regions of interest (ROIs). In total, 1,197 radiomic features were extracted from these ROIs using PyRadiomics. The most significant features were selected using logistic regression and Pearson correlation coefficient matrices. Using these features, we constructed three linear classification models based on the random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms, respectively. The training and test sets were repeatedly selected using fivefold cross-validation. The model performance was evaluated using the area under the receiver operator characteristic curve (AUC) and confusion matrix.
Results
The classification model based on RF had the highest performance, with an AUC of 0.994 (95% confidence interval [CI]: 0.979–1.00) for differentiating normal BMD and osteoporosis, 0.866 (95% CI: 0.779–0.954) for osteopenia versus osteoporosis, and 0.940 (95% CI: 0.891–0.989) for normal BMD versus osteopenia.
Conclusions
The excellent performance of this radiomic model indicates that lumbar spine CT images can effectively be used to identify osteoporosis and as a tool for opportunistic osteoporosis screening.
Background:
Bone development, particularly important during adolescence, can be affected by a variety of factors that can lead to the development of bone diseases such as osteoporosis or fractures. Whether dental caries is related to skeletal status, or whether lack of calcium affects the teeth, is always the question that clinical patients want to have answered. The present study was aimed to compare the bone mineral density and bone metabolism of adolescents with and without dental caries.
Methods:
Adolescents were enrolled in the dental caries and caries-free groups, respectively. A questionnaire and clinical oral examination in terms of DMFT scores were conducted. Bone mineral density (BMD) was tested and peripheral blood was collected for bone metabolism assessment.
Results:
119 and 140 adolescents were included in the dental caries and caries-free groups. The mean BMD and Z-score of the two groups showed no statistically significant difference. Serum concentrations of ALP, bone alkaline phosphatase, N-terminal osteocalcin, peptide of type I procollagen, and β-cross-linked C-telopeptide of type 1 collagen levels in the dental caries group were significantly lower than in the caries-free group (p < 0.05). Serum calcium, phosphorus, magnesium, 25-OH-VitD, and parathyroid hormone were not statistically different between two groups. Multiple factor logistic regression showed that serum calcium concentration in males had an OR of 2.55 for dental caries (p< 0.001).
Conclusions:
BMD and majority of bone metabolism indexes were not related with dental caries among Chinese adolescents. For male adolescents, serum calcium level was associated with higher risk of dental caries.
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