Details

Autor(en) / Beteiligte

• Crimi, Alessandro, PhD
• Loog, Marco, PhD
• de Bruijne, Marleen, PhD
• Nielsen, Mads, PhD
• Lillholm, Martin, PhD

Titel

Shape-based Assessment of Vertebral Fracture Risk in Postmenopausal Women Using Discriminative Shape Alignment

Ist Teil von

• Academic radiology, 2012-04, Vol.19 (4), p.446-454

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2012

Quelle

MEDLINE

Beschreibungen/Notizen

• 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 classifier, 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 classifier. Results The classifier tested on the study population quantified vertebral fracture risk, giving statistically significant results for the radiologist annotations (area under the curve, 0.71 ± 0.013; odds ratio, 4.9; 95% confidence interval, 2.94–8.05). Conclusions The shape-based classifier 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.