Reproducibility and postacquisition correction methods for quantitative magnetic resonance imaging of the anterior cruciate ligament (ACL)

Predicting anterior cruciate ligament failure load with T2* relaxometry and machine learning as a prospective imaging biomarker for revision surgery

Non-invasive methods to document healing anterior cruciate ligament (ACL) structural properties could potentially identify patients at risk for revision surgery. The objective was to evaluate machine learning models to predict ACL failure load from magnetic resonance images (MRI) and to determine if those predictions were related to revision surgery incidence. It was hypothesized that the optimal model would demonstrate a lower mean absolute error (MAE) than the benchmark linear regression model, and that patients with a lower estimated failure load would have higher revision incidence 2 years post-surgery. Support vector machine, random forest, AdaBoost, XGBoost, and linear regression models were trained using MRI T2* relaxometry and ACL tensile testing data from minipigs (n = 65). The lowest MAE model was used to estimate ACL failure load for surgical patients at 9 months post-surgery (n = 46) and dichotomized into low and high score groups via Youden's J statistic to compare revision incidence. Significance was set at alpha = 0.05. The random forest model decreased the failure load MAE by 55% (Wilcoxon signed-rank test: p = 0.01) versus the benchmark. The low score group had a higher revision incidence (21% vs. 5%; Chi-square test: p = 0.09). ACL structural property estimates via MRI may provide a biomarker for clinical decision making.

Quantitative MRI Biomarkers to Predict Risk of Reinjury Within 2 Years After Bridge-Enhanced ACL Restoration

BACKGROUND

Quantitative magnetic resonance imaging (qMRI) methods were developed to establish the integrity of healing anterior cruciate ligaments (ACLs) and grafts. Whether qMRI variables predict risk of reinjury is unknown.

PURPOSE

To determine if qMRI measures at 6 to 9 months after bridge-enhanced ACL restoration (BEAR) can predict the risk of revision surgery within 2 years of the index procedure.

STUDY DESIGN

Cohort study; Level of evidence, 2.

METHODS

Originally, 124 patients underwent ACL restoration as part of the BEAR I, BEAR II, and BEAR III prospective trials and had consented to undergo an MRI of the surgical knee 6 to 9 months after surgery. Only 1 participant was lost to follow-up, and 4 did not undergo MRI, leaving a total of 119 patients for this study. qMRI techniques were used to determine the mean cross-sectional area; normalized signal intensity; and a qMRI-based predicted failure load, which was calculated using a prespecified equation based on cross-sectional area and normalized signal intensity. Patient-reported outcomes (International Knee Documentation Committee subjective score), clinical measures (hamstring strength, quadriceps strength, and side-to-side knee laxity), and functional outcomes (single-leg hop) were also measured at 6 to 9 months after surgery. Univariate and multivariable analyses were performed to determine the odds ratios (ORs) for revision surgery based on the qMRI and non-imaging variables. Patient age and medial posterior tibial slope values were included as covariates.

RESULTS

In total, 119 patients (97%), with a median age of 17.6 years, underwent MRI between 6 and 9 months postoperatively. Sixteen of 119 patients (13%) required revision ACL surgery. In univariate analyses, higher International Knee Documentation Committee subjective score at 6 to 9 months postoperatively (OR = 1.66 per 10-point increase; P = .035) and lower qMRI-based predicted failure load (OR = 0.66 per 100-N increase; P = .014) were associated with increased risk of revision surgery. In the multivariable model, when adjusted for age and posterior tibial slope, the qMRI-based predicted failure load was the only significant predictor of revision surgery (OR = 0.71 per 100 N; P = .044).

CONCLUSION

Quantitative MRI-based predicted failure load of the healing ACL was a significant predictor of the risk of revision within 2 years after BEAR surgery. The current findings highlight the potential utility of early qMRI in the postoperative management of patients undergoing the BEAR procedure.