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Published on: March 11, 2017
Prediction of Osteoporosis and Fragility Fracture Risk Using Proximal Humerus CT Value from Chest CT: A Development
Chen-Xu Zhao1, Ren-An Wang2, Xiao Feng3
1Department of Radiology, General Hospital of Northern Theater Command, Shenyang 110016, China (C-X.Z., M-Y.Z., X-G.L., D.H., B-Q.Y., Y.S., L-B.Z.); Dalian Medical University 967 Hospital of the Joint Logistics Support Force Graduate Training Base, Dalian, China (C-X.Z.).
Rationale And Objectives:
To develop a nomogram based on the proximal humerus computed tomography (CT) value derived from routine chest CT scans, enabling opportunistic screening for osteoporosis and assessment of fragility fracture risk without additional cost or radiation exposure.
Materials And Methods:
This multicenter retrospective study analyzed data from 542 patients across three hospitals (397 in the training cohort, 145 in the validation cohort). A clinical model for osteoporosis and fracture risk was first established as a baseline. Using routine chest CT images, the proximal humerus CT value was measured and integrated with clinical variables via multivariable logistic regression to construct a combined Clinical + CT model. The nomogram was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis.
Results:
The Clinical + CT model significantly outperformed the clinical model in predicting fracture risk and diagnosing osteoporosis. For fracture risk prediction, the Clinical + CT model achieved the area under the curve (AUC) of 0.922 (95% CI: 0.895-0.949) in the training cohort and 0.914 (95% CI: 0.861-0.967) in the validation cohort. For osteoporosis diagnosis, the AUCs were 0.804 (95% CI: 0.761-0.847) and 0.800 (95% CI: 0.722-0.878), respectively.
Conclusion:
The Clinical + CT model, which integrates clinical factors with proximal humerus CT values from routine chest CT scans, can effectively identify individuals with osteoporosis and high fracture risk, offering a simple and effective tool for opportunistic screening. The models demonstrate good reliability, and decision curve analysis indicates a high net clinical benefit for both endpoints.
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