Predicting Thoracolumbar Vertebral Osteoporotic Fractures: Value Assessment of Chest CT-Based Machine Learning
Yue Chen1, Minghao Che1, Hui Yang1
1Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China (Y.C., M.C., H.Y., Z.Y., J.Q.).
Rationale And Objectives:
To assess the value of a chest CT-based machine learning model in predicting osteoporotic vertebral fractures (OVFs) of the thoracolumbar vertebral bodies.
Materials And Methods:
We monitored 8910 patients aged ≥50 who underwent chest CT (2021-2024), identifying 54 incident OVFs cases. Using propensity score matching, 108 controls were selected. The 162 patients were randomly assigned to training (n=113) and testing (n=49) cohorts. Clinical models were developed through logistic regression. Radiomics features were extracted from the thoracolumbar vertebral bodies (T11-L2), with top 10 features selected via minimum-redundancy maximum-relevancy and the least absolute shrinkage and selection operator to construct a Radscore model. Nomogram model was established combining clinical and radiomics features, evaluated using receiver operating characteristic curves, decision curve analysis (DCA) and calibration plots.
Results:
Volumetric bone mineral density (vBMD) (OR=0.95, 95%CI=0.93-0.97) and hemoglobin (HGB) (OR=0.96, 95%CI=0.94-0.98) were selected as independent risk factors for clinical model. From 2288 radiomics features, 10 were selected for Radscore calculation. The Nomogram model (Radscore + vBMD + HGB) achieved area under the curve (AUC) of 0.938/0.906 in training/testing cohorts, outperforming both Radscore (AUC=0.902/0.871) and clinical (AUC=0.802/0.820) models. DCA and calibration plots confirmed the Nomogram model's superior prediction capability.
Conclusion:
Nomogram model combined with radiomics and clinical features has high predictive performance, and its predictive results for thoracolumbar OVFs can provide reference for clinical decision making.
