A 3D STATISTICAL SHAPE MODEL TO DESCRIBE CLINICAL SHAPE VARIATION OF THE PROXIMAL FEMUR IN PATIENTS WITH LEGG-CALVÉ-PERTHES DISEASE DEFORMITY

Legg-Calvé-Perthes Disease (LCPD) often results in a permanent residual hip deformity, with long-term impacts on hip function. In the clinic, decisions on how to manage LCPD deformity are based on 2D radiographic measures such as the Stulberg grades. It is unclear whether these measures accurately represent complex, patient-specific 3D deformity, making it difficult to determine the best treatment approach. Statistical shape modeling (SSM) provides an objective and compact description of variability, but there is currently no 3D SSM that focuses solely on describing shape variability in LCPD. In this study, we have constructed an SSM of femurs with LCPD deformity, which will provide a foundation for future research into how deformity is measured in the clinic. Clinical magnetic resonance (MR) images (N=13 hips, 11 patients, 3 F/8 M) of affected hips were obtained in patients with stage IV LCPD (age range: 6–12 years, estimated Stulberg grades: II-V). MR volumes were resampled isotropically to the smallest voxel dimension (.47 – .63 mm), and two raters manually segmented the proximal femurs. ShapeWorks (SCI Institute, University of Utah, Salt Lake City, UT) was used to produce an SSM with 512 particles using an incremental optimization routine: corresponding particles were initially distributed and optimized on a subset of the five most similar femurs. The mean particle distribution of the initial model was then used to initialize the remaining eight shapes, followed by an additional optimization of the particle distribution. Differences in pose and scale between shapes were removed with generalized Procrustes analysis. Modes of shape variation were quantified using principal component analysis. The first four shape modes described 87.5% of the population variability, and qualitatively captured clinically relevant variables such as femoral head width and articular-trochanteric distance (see Figure 1). A significant association (p=0.04) was observed between shape mode 4 and asphericity of the femoral head (rho=0.58). A leave-one-out cross-validation of the model demonstrated good generalizability to unseen shapes, with a mean point-to-point reconstruction error of < 1 mm if > 4 modes were included. This SSM provides a continuous, accurate, and compact representation of 3D shape variation in LCPD. Limitations to this model include a small sample size, and thus the inability to control for age. In addition to expanding this model, future work will incorporate the acetabular side to describe joint congruence, and associate shape modes with radiographic parameters. For any figures or tables, please contact the authors directly.