Investigating the effect of metastatic lesions on the load-bearing capacity of the spinal column is essential for identifying appropriate interventions for patients. Most studies on the effects of tumor size and location on vertebral fractures are computational, with limited experimental data available. Furthermore, it remains unclear which parameters are most critical for assessing fracture risk in metastatic spines. The purpose of the current study was to investigate the effect of lesion size and location on fracture characteristics of the vertebral body and to find contributing factors associated with vertebral fractures. Human cadaveric spine segments were dissected from fresh frozen torsos and divided into three groups: intact, central defects affecting only trabecular bones, and anterior defects affecting both cortical and trabecular bones. Computed tomography imaging was performed to measure the geometry and Hounsfield Unit (HU) values and then, these specimens were mechanically tested to failure to record vertebral fracture forces. Compared to the intact specimens (2092 kPa), the average fracture strength in the defect groups were reduced by at least 750 kPa with a defect size as small as 25% of the cross-sectional area (CSA) or 9% of the volume (p=0.0097). Tumor location did not significantly affect vertebral fracture forces (p=0.77), as both locations primarily involved trabecular bone. The results of this study suggest that the vertebral body becomes significantly weaker even with simulated lytic defects involving as little as approximately 9% of the vertebral volume or 25% of the vertebral cross-sectional area (CSA). This degree of bone involvement may represent a new biomechanical threshold for assessing fracture risk in metastatic spines with bone loss. These findings underscore the importance of proactive assessment and potential early intervention in patients with small vertebral lytic lesions-particularly in individuals already at elevated risk of vertebral fracture due to conditions such as osteoporosis.

Abstract Purpose The aims of this study are (1) to derive a set of time series features to predict the total knee contact force during gait from two ankle-mounted inertial measurement units (IMUs); and (2) to explore the best combination of PROMs and IMU-derived, biomechanical measures for the prediction of postoperative quality of life (QoL) and reduce the inconsistency between biomechanical and PROMs. Methods Synced motion capture (optical and inertial) data were collected from four healthy participants to obtain a suitable time-series feature set that relates to knee contact forces (KCF). Then, using data from 28 patients during overground walking, we generated linear and random-forest regressor models for estimating QoL. These models were evaluated using temporal cross-validation. Results Overall, features from the IMUs could predict the total KCF (R 2 > 0.90, RMSE = 0.14, %BW). A machine learning model was trained on the data obtained during the recovery (up to 1 year) to predict EQ-5D-5 L score (patient QoL). The models were able to predict within a margin of 1.81–4.40 units of the actual score 95% of the time. Conclusion The presented study derives surrogate measures of total knee contact force (KCF) using ankle-mount IMUs. We identified the best combination of PROMs and IMU-derived measures for postoperative QoL at four time-points. The optimal metrics varied for each time point: OKS only, knee kinematics with OKS, KCF with OKS, and KCF only. Further data collection will be required to compile a comprehensive dataset to generate a robust predictive model.