Using Statistical Shape Modeling to Evaluate Anatomic Variations Underlying the J-Sign in Patella Instability Patients

For patients with patellofemoral instability, the simplest surgical option is often medial patellofemoral ligament (MPFL) reconstruction without regard to the patients’ bony alignment. However, there is no high-level evidence on which cohorts would likely have a better outcome from more comprehensive procedures like derotational distal femoral osteotomy.

Research shows that a high-grade J-sign, one of the hallmark signs of patellar maltracking, is associated with worse outcomes following MPFL reconstruction. The J-sign presents as noticeable lateral movement of the patella — similar to an inverted J — in the final stages of knee extension during physical examination. Orthopedic surgeons grade J-signs on a scale from 1 to 4 based on the number of quadrants in which the patella moves during extension. Even though the J-sign is widely used, it is inherently subjective.

Adam Yanke, MD, PhD, a sports medicine orthopedic surgeon with a special focus on patellofemoral dysfunction and cartilage restoration, and other Rush researchers sought to investigate a novel approach for analyzing anatomical risk factors that could affect the pathogenesis of patellofemoral instability: statistical shape modeling (SSM).

SSM as an emerging tool

Compared with conventional radiography techniques, SSM is a more powerful method for analyzing the anatomic variations across a population, as it allows for detailed, 2D or 3D analysis of the intricate shapes and relative alignment of the patella, tibia and femur, Dr. Yanke explains.

Dr. Yanke’s study is the first to apply SSM to evaluate the full range of anatomical differences in patients with patella instability. “At Rush, we have a unique ability to do this type of study because we have a high volume of patients with this condition as well as high-quality imaging and scientists who understand how to utilize this method,” Dr. Yanke says.

This study builds on work by Fitzpatrick, et al, which demonstrated the value of SSM in analyzing the relationship between joint shape and function in the patellofemoral joint.

The study’s aim and methodology

Dr. Yanke’s team conducted a retrospective review study on 3D supine magnetic resonance imaging (MRI) scans from 15 patients with patellofemoral instability. For secondary analysis, they used imaging from four healthy controls. Dr. Yanke clinically assessed J-sign and organized patients into four groups based on J-sign severity.

For the SSM, the team used preoperative images of each patient’s distal femur, full patella and proximal tibia to build a multidomain model that included the bones together, as well as three separate models for each individual bone. Optimization was performed with a total of 9,216 correspondence points across all three bones. The SSM provided mean shapes for each joint across all patient models, as well as mean positions of each bone relative to each other. The team performed a linear discriminant analysis to assess differences among patient groups based on J-sign.

Results

Dr. Yanke’s team found that increasing J-sign severity was associated with more lateral displacement of the patella with respect to the femur. They also noted significant differences in the depth of the trochlear groove across J-sign groups. “The average femoral shape in the highest J-sign severity group displayed a convex trochlear morphology — kind of a ‘dome’ rather than a groove,” Dr. Yanke explains, adding that this finding reinforces previously described associations between severe trochlear dysplasia and pathologic patellar tracking by Dandu, et al.

With increasing J-sign severity, the team also observed increasing sulcus angle, central trochlear height, trochlear dysplasia, and tibial tubercle lateralization.

Overall, the research findings support the hypothesis that high-grade J-signs reflect a distinct anatomical phenotype of dynamic maltracking.

Beyond presenting a novel application of SSM, the study demonstrates the limitations of relying on a single measurement like J-sign to indicate osteotomy. “Our clinical evaluation of J-signs are not reliable enough because they are subjective, whereas there’s no subjective component to automatic statistical shape modeling,” Dr. Yanke explains.

The study also supports the development of more individualized surgical strategies based on patients’ anatomy. “Not everyone who has a J-sign necessarily needs an osteotomy to correct it, even though we know there are multiple anatomic issues that can be related to having that J-sign. Many patients do well with less, and yet we shouldn’t make decisions based on individual physical exam findings.”

Next steps

Dr. Yanke is collaborating with researchers at Stanford on another dataset to validate their model outside of Rush. Once validated, the findings could be used to determine which patients with patellar instability would most likely benefit from an osteotomy, Dr. Yanke says.