Fracture risk predictions based on statistical shape and density modeling of the proximal femur.

Increased risk of skeletal fractures due to bone mass loss is a major public health problem resulting in significant morbidity and mortality, particularly in the case of hip fractures. Current clinical methods based on two-dimensional measures of bone mineral density (areal BMD or aBMD) are often unable to identify individuals at risk of fracture. We investigated predictions of fracture risk based on statistical shape and density modeling (SSDM) methods using a case-cohort sample of individuals from the Osteoporotic Fractures in Men (MrOS) study. Baseline quantitative computed tomography (QCT) data of the right femur were obtained for 513 individuals, including 45 who fractured a hip during follow-up (mean 6.9 year observation, validated by physician review). QCT data were processed for 450 individuals (including 40 fracture cases) to develop individual models describing three-dimensional bone geometry and density distribution. Comparison of mean fracture and non-case models indicated complex structural differences that appear to be responsible for resistance to hip fracture. Logistic regressions were used to model the relation of baseline hip BMD and SSDM weighting factors to the occurrence of hip fracture. Area under the receiver operating characteristic (ROC) curve (AUC) for a prediction model based on weighting factors and adjusted by age was significantly greater than AUC for a prediction model based on aBMD and age (0.94 versus 0.83, respectively). The SSDM-based prediction model adjusted by age correctly identified 55% of the fracture cases (and 94.7% of the non-cases), whereas the clinical standard aBMD correctly identified 10% of the fracture cases (and 91.3% of the non-cases). SSDM identifies subtle changes in combinations of structural bone traits (eg, geometric and BMD distribution traits) that appear to indicate fracture risk. Investigation of important structural differences in the proximal femur between fracture and no-fracture cases may lead to improved prediction of those at risk for future hip fracture.

Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the osteoarthritis initiative database.

We hypothesize that variability in knee subchondral bone surface geometry will differentiate between patients at risk and those not at risk for developing osteoarthritis (OA) and suggest that statistical shape modeling (SSM) methods form the basis for developing a diagnostic tool for predicting the onset of OA. Using a subset of clinical knee MRI data from the osteoarthritis initiative (OAI), the objectives of this study were to (1) utilize SSM to compactly and efficiently describe variability in knee subchondral bone surface geometry and (2) determine the efficacy of SSM and rigid body transformations to distinguish between patients who are not expected to develop osteoarthritis (i.e. Control group) and those with clinical risk factors for OA (i.e. Incidence group). Quantitative differences in femur and tibia surface geometry were demonstrated between groups, although differences in knee joint alignment measures were not statistically significant, suggesting that variability in individual bone geometry may play a greater role in determining joint space geometry and mechanics. SSM provides a means of explicitly describing complete articular surface geometry and allows the complex spatial variation in joint surface geometry and joint congruence between healthy subjects and those with clinical risk of developing or existing signs of OA to be statistically demonstrated.