Characterizing Osteosarcopenia In Spinal Metastases Patients Undergoing Stereotactic Body Radiotherapy (Sbrt): Leveraging Deep Learning For Improved Outcome Prediction

Stereotactic body radiotherapy (SBRT) is commonly used to treat spinal metastases, offering excellent local control and pain relief. However, it carries an average 14% risk of vertebral compression fractures (VCFs), and despite growing evidence linking osteosarcopenia to adverse clinical outcomes, musculoskeletal health is not routinely assessed during SBRT treatment planning. This thesis introduces a fully automated pipeline for extracting musculoskeletal biomarkers from CT, combining deep learning–based segmentation with vertebral landmark–guided cropping and volumetric analysis. Sarcopenia thresholds were derived for volumetric indices using height-based and vertebral-based normalization, guided by established literature cutoffs for the Psoas Muscle Index (PMI). Osteoporosis was defined using trabecular bone density. In this SBRT cohort, 58% of patients met criteria for osteoporosis, 45% for sarcopenia, and 31% for osteosarcopenia. In multivariable logistic regression analyses, significant associations between fracture risk and both osteoporosis and lower psoas muscle density were observed in specific models, warranting further investigation. Additionally, categorical definitions of sarcopenia and osteosarcopenia were significantly associated with reduced overall survival. The pipeline was extended to MRI using CT-based segmentations as weak labels for training nnU-Net models, achieving high segmentation accuracy and supporting future radiation-free musculoskeletal biomarker assessment.