Improving Osteoporosis Prediction Using Vertebral Bone Quality Score and Paravertebral Muscle Measurements From Lumbar MRI Scans

Study Design: Retrospective cohort. Objective: This study aims to use a novel method of combining vertebral bone quality score with paravertebral cross-sectional area measurements to improve the accuracy of predicting individuals with total hip T-scores <−2.5. Summary of Background Data: Osteoporosis is a prevalent skeletal condition associated with decreased bone density and increased fracture risk. Dual-energy x-ray absorptiometry (DXA) is the conventional method for diagnosing osteoporosis, but it has limitations. Opportunistic osteoporosis screening techniques using lumbar magnetic resonance imaging (MRI), particularly the vertebral bone quality (VBQ) score, have shown promise. This study aims to improve the accuracy of predicting individuals with low total hip T-scores using a novel method that combines VBQ scores with paravertebral cross-sectional area (CSA) measurements. Methods: A retrospective cohort of 98 patients with DXA and lumbar MRI scans was analyzed. VBQ scores were calculated based on lumbar MRI images, and CSA measurements of paravertebral and psoas muscles were obtained. Threshold-based logistic regression was used to identify optimal thresholds for predicting total hip T-scores <−2.5. Results: The combined model incorporating the VBQ score and paravertebral muscle percent achieved an accuracy of 96.9% for predicting total hip T-scores <−2.5, compared to 81.6% when using the VBQ score alone. Incorporating paravertebral muscle measurements significantly improved the accuracy of identifying osteoporotic individuals. Conclusions: The combination of VBQ score and paravertebral muscle measurements enhances the accuracy of predicting individuals with low total hip T-scores. Lumbar MRI scans provide valuable information beyond opportunistic osteoporosis screening, and the inclusion of paravertebral muscle measurements could aid in identifying at-risk individuals more accurately.