The risk assessment of pathological fracture in the proximal femur using a CT-based finite element method.

ABSTRACT

BACKGROUND: Patients who have lytic bone lesions in their proximal femurs are at risk for pathological fracture. Lesions with high fracture risk are surgically treated using prophylactic osteosynthesis, whereas low-risk lesions are treated conservatively. However, it is difficult to discriminate between high- and low-risk lesions based on clinical and radiographic findings. The computed tomography (CT)-based finite element (FE) models are useful for predicting the fracture load on proximal femoral lytic lesions. MATERIALS AND METHODS: FE models were constructed from the quantitative CT scans of the femurs using software that created individual bone shapes and density distributions. Three independent observers measured the lesion size, Mirels' score, and thickness of the proximal femur along the horizontal plane. The predictive risk values of the proximal femur measured using the CT-based FE analysis were statistically compared. RESULTS: The patients were divided into two groups (high and low risk). The mean fracture load was significantly higher in the high-risk group than in the low-risk group (5395 ± 525 N, 2622 ± 364 N, respectively, p = 0.0003). No significant differences in age, body weight, lesion size or Mirels' score were observed between groups. However, the thickness of the medial cortex in the high-risk group according to the FE analysis was significantly thinner than that in the low-risk group. Furthermore, the medial cortex thickness was positively correlated with the predicted fracture load. An optimal cut-off value of 3.67 mm for the thickness of the inner cortex resulted in 100% sensitivity and 75.1% specificity values for classifying the patients based on their fracture risk. CONCLUSIONS: Our findings indicate that the FE method is useful for the prediction of the pathological fracture. This method shows a versatile potential for the prediction of pathological fracture and might aid in judging the optimal treatment to prevent fracture.