Introduction: Pathologic vertebral fractures are devastating for patients with spinal metastases. However, the mechanical process underlying these fractures is poorly understood, limiting physician\'s ability to predict which vertebral bodies will fail. Method: Here, we show the development of a damage-based finite element framework producing highly reliable pathologic vertebral strength and stiffness predictions from X-Ray computed tomography (CT) data. We evaluated the performance of specimen-specific material calibration vs. global material calibration across osteosclerotic, osteolytic, and mixed lesion vertebrae that we derived using a machine learning approach. Results: The FE framework using global calibration strongly predicted the pathologic vertebrae stiffness (R 2 = 0.90, p < 0.0001) and strength (R 2 = 0.83, p = 0.0002) despite the remarkable variance in the pathologic bone structure and density. Specimen-specific calibration produced a near-perfect prediction of both stiffness and strength (R 2 = 0.99, p < 0.0001, for both), validating the FE approach. The FE damage-based simulations highlighted the differences in the pattern of spatial damage evolution between osteosclerotic and osteolytic vertebral bodies. Discussion: With failure, the FE simulation suggested a common damage evolution pathway progressing largely localized to the low bone modulus regions within the vertebral volume. Applying this FE approach may allow us to predict the onset and anatomical location of vertebral failure, which is critical for developing image-based diagnostics of impending pathologic vertebral fractures.

The deep drawing process, a pivotal technique in sheet metal forming, frequently encounters challenges such as anisotropy-induced defects. This study comprehensively investigates the influence of various yield criteria on the anisotropic behavior and fracture prediction in SECC steel cylindrical cups. It integrates Hill\'48R, Hill\'48S, and von Mises yield criteria in conjunction with Swift\'s hardening law to evaluate material behavior under complex stress states. Experimental and numerical simulations assess the anisotropy effects across multiple orientations (0°, 45°, and 90°), revealing intricate relationships between stress criteria and material response. The findings indicate significant discrepancies between isotropic and anisotropic models in predicting fracture heights, emphasizing the importance of selecting appropriate yield criteria. Notably, the von Mises criterion results in lower fracture heights, suggesting higher susceptibility to fractures, while the Hill\'48R model aligns closely with experimental data, validated through variations in punch corner radius and blank holder force parameters, with a maximum deviation of 3.23%. Hill\'48S displays moderate plastic deformation characteristics.

In the longitudinal, retrospective study, the ability of the FRAX, Garvan, and POL-RISK algorithms to predict osteoporotic fractures was compared in a group of 457 women. Using the rigid threshold of 10% showed a significant discrepancy in sensitivity and specificity of all tools. New thresholds for high risk of fractures were established for each calculator separately: 6.3% for FRAX major fracture, 20.0% for Garvan any fracture, and 18.0% for POL-RISK any fracture. Such thresholds allow for improving the diagnostic accuracy of all three calculators.
BACKGROUND: The aim of the longitudinal, retrospective study was to compare three tools designed to assess fracture risk: FRAX, Garvan, and POL-RISK in their prediction of fracture incidence.
METHODS: The study group consisted of 457 postmenopausal women with a mean age of 64.21 ± 5.94 years from the Gliwice Osteoporosis (GO) Study. Comprehensive data on clinical factors related to fractures were collected for all participants. Bone densitometry was performed at the proximal femur using the Prodigy device (GE, USA). Fracture risk was established using the FRAX, Garvan, and POL-RISK algorithms. Data on the incidence of osteoporotic fractures were collected over the last 10 years.
RESULTS: During the period of observation 72, osteoporotic fractures occurred in 63 subjects. For a preliminary comparison of the predictive value of analyzed diagnostic tools, the fracture risk threshold of 10% was used. For FRAX, the fracture probability exceeding 10% was observed only in 11 subjects who experienced fractures; thus, the fracture was properly predicted only in 22.9% of women. For Garvan, the respective value was 90.5%, and for POL-RISK, it was 98.4%. That gave a very low true positive value for FRAX and a very high false positive value for Garvan and POL-RISK. Based on ROC curves, new thresholds for high risk of fractures were established for each calculator separately: 6.3% for FRAX major fracture, 20.0% for Garvan any fracture, and 18.0% for POL-RISK any fracture. Such thresholds improve the diagnostic accuracy of all compared fracture prediction tools.
CONCLUSIONS: The current study showed that different fracture risk assessment tools, although having similar clinical purposes, require different cut-off thresholds for making therapeutic decisions. Better identification of patients requiring therapy based on such an approach may help reduce the number of new fractures.

Bone mineral density (BMD) is widely used for assessment of fracture risk. For the lumbar spine, BMD is typically measured from L1-L4 as it provides the largest area for assessment with the best measurement precision. Structural artifact often confounds spine BMD in clinical practice, and the International Society for Clinical Densitometry (ISCD) recommends removing vertebrae with artifact when reporting spine BMD. In its most recent position statements, the ISCD recommended against the use of a single vertebra when reporting spine BMD but stated that further studies should be done. The current analysis was performed to compare the performance of BMD from different numbers and combination of vertebral levels on fracture prediction in a large clinical registry of DXA tests for the Province of Manitoba, Canada. The study population comprised 39,727 individuals aged 40 years and older (mean age 62.7 years, 91.0 % female) with baseline DXA after excluding those with evidence of structural artifact. Mean follow-up for ascertaining fracture outcomes was 8.7 years. Area under the curve (AUC) for incident fracture risk stratification was statistically significant regardless of the BMD measurement site or fracture outcome. AUC differences with the various numbers and combinations of vertebral levels including a single vertebral body were small (less than or equal to 0.01). More substantial AUC differences were seen for femoral neck and total hip BMD versus L1-L4 BMD, approaching 0.1 for hip fracture stratification. In summary, we found that using combinations of fewer than 4 vertebrae including individual lumbar vertebrae predicted incident fractures. Importantly, differences between these different combinations were small when compared with L1-L4. Spine BMD was a better predictor of incident spine fracture compared to the hip, whereas the hip was better for hip fracture and overall fracture prediction.

UNASSIGNED: Accurate comparison of prophylactic surgical treatment (PST) to after fracture treatment (AF) of patients with femoral metastatic disease requires more accurately identifying patients for impending fracture, such as with CT-based structural rigidity analysis (CTRA). This study compares a more accurately defined PST group (of impending fractures defined by CTRA) to AF for metastatic femoral disease.
UNASSIGNED: PST patients were enrolled and treated by the PI in a longitudinal multicenter study of impending pathologic fractures evaluated for accuracy by CTRA. The AF patients were also treated by the senior author and were identified by retrospective chart review. Fifty-five patients were treated surgically for metastatic femoral lesions and were divided into three groups for the purpose of this study: Group I (AF), Group II (PST-high), and Group III (PST-low). Demographic information, comorbidities, and clinical variables of interest were collected by retrospective chart review; cost data was collected by collaboration with our hospital financial personnel (office of the Chief Financial Officer).
UNASSIGNED: Survival showed statistically significant differences favoring Group II. Transfusions in Group I were nearly twice those of Groups II and III, but there was no statistically significant (NS) difference between groups. Estimated blood loss (EBL) was generally with NS difference. Similarly, there were NS differences in LOS between groups. Discharge disposition showed statistically significant differences between groups (P=0.012, global). Discharge to home was highest in Group II (76%) and lowest in Group I (27%). Discharge to rehab was lowest in Group II (24%) and highest in Group I (47%). There were no discharges to hospice or morgue in Group II, while both occurred in Group I. Mean direct and total costs were highest in Group I ($18,837 and $31,997, respectively) and lowest in Group II ($16,094 and $27,357) but the differences were NS.
UNASSIGNED: This study shows benefits of PST over AF in a group of PST patients more accurately defined to have impending pathologic fractures by CTRA definition.

The aim of this study was to determine whether the Bone Strain Index (BSI), a recent DXA-based bone index, is related to bone mechanical behavior, microarchitecture and finally, to determine whether BSI improves the prediction of bone strength and the predictive role of BMD in clinical practice.
OBJECTIVE: Bone Strain Index (BSI) is a new DXA-based bone index that represents the finite element analysis of the bone deformation under load. The current study aimed to assess whether the BSI is associated with 3D microarchitecture and the mechanical behavior of human lumbar vertebrae.
METHODS: Lumbar vertebrae (L3) were harvested fresh from 31 human donors. The anteroposterior BMC (g) and aBMD (g/cm2) of the vertebral body were measured using DXA, and then the BSI was automatically derived. The trabecular bone volume (Tb.BV/TV), trabecular thickness (Tb.Th), degree of anisotropy (DA), and structure model index (SMI) were measured using µCT with a 35-µm isotropic voxel size. Quasi-static uniaxial compressive testing was performed on L3 vertebral bodies under displacement control to assess failure load and stiffness.
RESULTS: The BSI was significantly correlated with failure load and stiffness (r = -0.60 and -0.59; p < 0.0001), aBMD and BMC (r = -0.93 and -0.86; p < 0.0001); Tb.BV/TV and SMI (r = -0.58 and 0.51; p = 0.001 and 0.004 respectively). After adjustment for aBMD, the association between BSI and stiffness, BSI and SMI remained significant (r = -0.51; p = 0.004 and r = -0.39; p = 0.03 respectively, partial correlations) and the relation between BSI and failure load was close to significance (r = -0.35; p = 0.06).
CONCLUSIONS: The BSI was significantly correlated with the microarchitecture and mechanical behavior of L3 vertebrae, and these associations remained statistically significant regardless of aBMD.

BACKGROUND: In daily practice the diagnostic process for osteoporosis in elderly patients should also include physical assessment. The aim of the study was to verify the hypothesis that height loss (HL) predicts fracture incidence.
METHODS: The study was performed in an epidemiological sample of postmenopausal women recruited in the RAC-OST-POL study. At baseline, data were collected in 978 postmenopausal women at a mean age of 66.48±7.6 years, and at 10-year follow-up 640 patients remained, with a mean age of 75.04 ± 6.95 years. Current height and HL were established in regard to maximal life height. Data on fracture incidence were gathered throughout the period of observation.
RESULTS: During the follow-up period 190 osteoporotic fractures were noted. Ninety-one women had one fracture, and in 38 women, multiple fractures occurred. In the fractured and unfractured subgroups, HL was 5.45 ± 3.28 and 4.8 ± 3.56 cm, respectively, and differed significantly (p < 0.05). HL in subjects without fracture did not differ from those with one fracture (HL 4.8 ± 3.56 vs. 4.8 ± 2.66 cm, respectively). For patients with more than one fracture HL was 7.03 ± 4.06 cm and was significantly higher than in subjects with one or without any fracture (p < 0.01). Based on receiver operating characteristic (ROC) analysis, HL of 6 cm was identified as the cut-off point for high risk of multiple fractures.
CONCLUSIONS: HL of at least 6 cm is the predictor of multiple fractures in a prospective observation of a representative epidemiological female sample. Therefore, the measurement of HL should always be included in patients\' assessments.

Areal bone mineral density (aBMD) currently represents the clinical gold standard for hip fracture risk assessment. Nevertheless, it is characterised by a limited prediction accuracy, as about half of the people experiencing a fracture are not classified as at being at risk by aBMD. In the context of a progressively ageing population, the identification of accurate predictive tools would be pivotal to implement preventive actions. In this study, DXA-based statistical models of the proximal femur shape, intensity (i.e., density) and their combination were developed and employed to predict hip fracture on a retrospective cohort of post-menopausal women. Proximal femur shape and pixel-by-pixel aBMD values were extracted from DXA images and partial least square (PLS) algorithm adopted to extract corresponding modes and components. Subsequently, logistic regression models were built employing the first three shape, intensity and shape-intensity PLS components, and their ability to predict hip fracture tested according to a 10-fold cross-validation procedure. The area under the ROC curves (AUC) for the shape, intensity, and shape-intensity-based predictive models were 0.59 (95%CI 0.47-0.69), 0.80 (95%CI 0.70-0.90) and 0.83 (95%CI 0.73-0.90), with the first being significantly lower than the latter two. aBMD yielded an AUC of 0.72 (95%CI 0.59-0.82), found to be significantly lower than the shape-intensity-based predictive model. In conclusion, a methodology to assess hip fracture risk uniquely based on the clinically available imaging technique, DXA, is proposed. Our study results show that hip fracture risk prediction could be enhanced by taking advantage of the full set of information DXA contains.

The effectiveness of a ductile fracture model in accurately predicting fracture initiation has been demonstrated. In this study, we concentrate on applying the ductile fracture model to pre-cracked structures constructed from SUS304L stainless steel with experimental and numerical analyses. The Swift hardening law was employed to extend the plastic behavior beyond the onset of necking. Additionally, the Hosford-Coulomb model, integrated with a damaged framework, was utilized to predict ductile fracture behavior, particularly under non-proportional loading conditions. Tension tests were conducted on various specimens designed to illustrate various fracture modes resulting from geometric effects. Numerical analyses were conducted to explore the loading histories, utilizing an optimization process to calibrate fracture model parameters. The proposed fracture model is validated against pre-cracked structures detailed in a reference paper. The results convincingly demonstrate that the fracture model effectively predicts both fracture initiation and propagation in pre-cracked structures.

Osteoporotic fractures impose a substantial burden on patients with diabetes due to their unique characteristics in bone metabolism, limiting the efficacy of conventional fracture prediction tools. Artificial intelligence (AI) algorithms have shown great promise in predicting osteoporotic fractures. This review aims to evaluate the application of traditional fracture prediction tools (FRAX, QFracture, and Garvan FRC) in patients with diabetes and osteoporosis, review AI-based fracture prediction achievements, and assess the potential efficiency of AI algorithms in this population. This comprehensive literature search was conducted in Pubmed and Web of Science. We found that conventional prediction tools exhibit limited accuracy in predicting fractures in patients with diabetes and osteoporosis due to their distinct bone metabolism characteristics. Conversely, AI algorithms show remarkable potential in enhancing predictive precision and improving patient outcomes. However, the utilization of AI algorithms for predicting osteoporotic fractures in diabetic patients is still in its nascent phase, further research is required to validate their efficacy and assess the potential advantages of their application in clinical practice.