Opportunistic Computed Tomography to Identify Osteoporosis in Primary Hyperparathyroidism Using Sestamibi SPECT/CT Scans

Abstract Background Osteoporosis is a well-recognised complication of primary hyperparathyroidism (PHPT). We aim to develop an osteoporosis prediction model for PHPT patients using opportunistic CT-derived Hounsfield unit (HU) measurements from sestamibi SPECT/CT. Methods Sestamibi SPECT/CT images from 81 PHPT patients (60 with and 21 without osteoporosis diagnosed by dual-energy x-ray absorptiometry) were analysed retrospectively. HU of vertebral bodies were measured. Receiver operating characteristics (ROC) analysis was performed to determine the optimal HU cutoff for each vertebra in predicting osteoporosis. Multivariable logistic regression was used to identify independent clinical parameters associated with osteoporosis. A prediction model based on clinical parameters alone was built. Another prediction model was constructed by further incorporating the HU of the vertebra. Model performance improvement was assessed. Results Age and body mass index (BMI) were independent risk factors for osteoporosis. The clinical prediction model (age and BMI) achieved an area under ROC (AUROC) of 0.874. Incorporation of HU of T4 vertebra into the clinical model showed the best performance with AUROC 0.966, sensitivity 98.2% and specificity 85.7%, representing a significant improvement in prediction with change in C-statistics 0.092 (95%CI 0.021–0.163, p = 0.011), net reclassification index 0.346 (0.041–0.650, p = 0.026) and incremental discrimination index 0.329 (0.116–0.542, p = 0.003). After bootstrap internal validation, the optimism-corrected AUROC was 0.949 (0.882–1.000). Conclusion Our study demonstrated the feasibility of integrating vertebral HU measurements from sestamibi SPECT/CT into a clinical prediction model for accurately identifying osteoporosis in PHPT patients, offering a potential tool for efficient bone health management and prioritising timely surgical interventions.