فهرست نشریات علمی دسترسی آزاد ایران

Background: Osteoporosis is a systemic skeletal disorder marked by reduced bone density and microarchitectural deterioration, leading to increased fracture risk. While the dual-energy X-ray absorptiometry (DEXA) scan is the World Health Organization (WHO)-recommended diagnostic standard, its limitations necessitate alternative methods. Emerging magnetic resonance imaging (MRI) techniques, radiomics, and machine learning promise to enhance osteoporosis diagnosis through detailed analysis of lumbar MRI apparent diffusion coefficient (ADC) maps, potentially revolutionizing early detection and treatment strategies. Objectives: In this study, we are going to evaluate the performance of machine learning (ML) models using radiomics features of lumbar MRI ADC map for osteoporosis detection, and to identify significant features and their diagnostic thresholds. Specific performance metrics such as accuracy, sensitivity, specificity, and Area Under the receiver operating characteristic (ROC) Curve (AUC) were assessed. Results: The study included 140 cases, with 132 females (94.3%) and 8 males (5.7%), and a mean age of 65.32 ± 8.50 years. The mean BMI was 31.43 ± 5.53 kg/m² for females and 26 ± 3.59 kg/m² for males. In terms of demographic differences, no significant age difference was found between the osteoporotic and non-osteoporotic groups (P = 0.889). However, the osteoporotic group had significantly lower mean body weight (64.90 ± 10.13 kg vs. 74.68 ± 13.94 kg, P = 0.003) and BMI (27.40 ± 4.38 kg/m² vs. 31.77 ± 5.52 kg/m², P = 0.001) compared to the non-osteoporotic group. The median interval between DEXA and lumbar MRI was 1 month (range 0.1 - 11.87 months). The Neural Network model demonstrated the highest performance with an AUC of 0.616 and a classification accuracy of 0.764 using all features. The Naive Bayes model, using the top five features selected by FCBF, showed the highest performance with an AUC of 0.913, accuracy of 0.907, sensitivity of 0.667, and specificity of 0.95. All ML models’ performance were elevated by feature selection. Independent t -tests and Mann-Whitney U tests identified 521 and 670 significant features, respectively (P < 0.05). ROC analysis revealed 58 features with AUC values above 0.70. Conclusion: This study's findings suggest that ML models, particularly the Naive Bayes algorithm, can effectively use lumbar ADC map radiomics to diagnose osteoporosis. These findings could enhance early detection and treatment strategies, potentially improving patient outcomes and reducing the burden of osteoporotic fractures. This study also established threshold values for significant features.