Year: 2025
Type: article
Study design: Observational Study
Abstract: OBJECTIVES: To evaluate a semiautomatic quantitative analysis approach using 4D CT for patellofemoral joint motion in patients with and without instability risk factors by classifying them into four subgroups: patellofemoral pain syndrome (PFPS), potential patellar instability (PPI), objective patellar instability (OPI) and control. MATERIALS & METHODS: This prospective monocentric case-control study included patients who presented with suspected patellofemoral instability from March 2019 to December 2023. Bilateral 4D CT was performed, radiographs were acquired, and the patella tilt angle (PTA) and patellofemoral distance (PFD) were measured during knee flexion-extension cycles. Based on radiographic and clinical findings, patients were divided into the following four groups: patellofemoral pain syndrome (PFPS), potential patellar instability (PPI), objective patellar instability (OPI), and a control group. Statistical analyses were performed to compare PTA and PFD across subgroups. RESULTS: A total of 78 patients (28 men, 50 women; mean age 34.2 ± 10.6 years) were included in the study population, with 116 knees. Interobserver variability was considered substantial for all PTA-derived parameters (ICC varying from 0.61 to 0.69) and fair for all PFD-derived parameters (ICC varying from 0.23 to 0.42). PTA and PFD were significantly greater in the PPI and OPI groups than in the PFPS and control groups (p < 0.01). The maximum PTA values showed the best diagnostic performance for the identification of patellofemoral instability risk factors, with a sensitivity of 71 % and specificity of 88 % at a threshold of 26.6° [confidence interval: 0.70, 0.88]. This first described threshold may help clinicians to distinguish real femoro patellar maltracking from normal kinematics. CONCLUSION: Dynamic 4D CT effectively identified significant variations in patellar motion between patients with and without risk factors for patellofemoral instability.
Source: European Journal of Radiology
Authors: Matthieu Lumia, Pedro Augusto Gondim Teixeira, Gabriela Hossu, Romain Gillet, Nicolas Douis, Alain Blum, Andrea Fernandez
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,440
OpenAlex ID: W4415215792
