CT-based assessment of posterior ligamentous complex integrity in AO spine type A1–A2 thoracolumbar junction fractures under conditions of diagnostic uncertainty

Abstract

Purpose: The diagnosis of posterior ligamentous complex (PLC) injury in thoracolumbar fractures remains a major challenge under conditions of diagnostic uncertainty, particularly when magnetic resonance imaging (MRI) is unavailable or delayed. This study aimed to develop and internally validate a quantitative CT-based model and a clinical nomogram for predicting PLC status in AO Spine type A1–A2 fractures using machine learning–guided morphometric analysis. Methods: CT and MRI data from 90 patients with thoracolumbar junction injuries were retrospectively analyzed, including 43 patients with AO Spine type A1–A2 fractures used for model development. Key morphometric parameters, including the acute interspinous expansion angle (AIEA), anterior-to-posterior height ratio (A/P), and bone density expressed in Hounsfield units (HU), were measured. An extreme gradient boosting (XGBoost) algorithm was used to estimate feature importance, which subsequently informed the development of an interpretable 10-point scoring scale and a logistic regression–based nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and calibration analysis with bootstrap correction. Results: The scoring scale identified AIEA and the A/P ratio as the most influential predictors, contributing 10.0 and 8.5 points, respectively. The score-based model demonstrated high discriminatory performance with an AUC of 0.944 (95% CI: 0.912–0.976), exceeding that of the baseline machine learning model (AUC = 0.836). Each additional point on the scale was associated with a 31% increase in the odds of PLC injury (odds ratio 1.31, p < 0.05). Calibration analysis showed good agreement between predicted and observed probabilities across the clinically relevant risk range (MAE = 0.041). Conclusion: The proposed CT-based prognostic model provides an objective and transparent tool for risk stratification in AO Spine type A1–A2 thoracolumbar fractures. By quantifying the probability of occult posterior instability, the nomogram supports evidence-based decision-making regarding the need for MRI and surgical stabilization in acute thoracolumbar trauma.