Rationale and Objectives: Risk assessment of future osteoporotic vertebral fractures is currently based mainly on risk factors, such as
bone mineral density, age, prior fragility fractures, and smoking. It can be argued that an osteoporotic vertebral fracture is not exclusively an
abrupt event but the result of a decaying process. To evaluate fracture risk, a shape-based classi¿er, identifying possible small prefracture
deformities, may be constructed.

Materials and Methods: During a longitudinal case-control study, a large population of postmenopausal women, fracture free at baseline,
were followed. The 22 women who sustained at least one lumbar fracture on follow-up represented the case group. The control group
comprised 91 women who maintained skeletal integrity and matched the case group according to the standard osteoporosis risk factors.
On radiographs, a radiologist and two technicians independently performed manual annotations of the vertebrae, and fracture prediction
using shape features extracted from the baseline annotations was performed. This was implemented using posterior probabilities from
a standard linear classi¿er.

Results: The classi¿er tested on the study population quanti¿ed vertebral fracture risk, giving statistically signi¿cant results for the
radiologist annotations (area under the curve, 0.71 0.013; odds ratio, 4.9; 95% con¿dence interval, 2.94–8.05).
Conclusions: The shape-based classi¿er provided meaningful information for the prediction of vertebral fractures. The approach was
tested on case and control groups matched for osteoporosis risk factors. Therefore, the method can be considered an additional
biomarker, which combined with traditional risk factors can improve population selection (eg, in clinical trials), identifying patients with
high fracture risk.