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Updated: Jul 11, 2025
Proximal Cadaveric Femur Preparation for Fracture Strength Testing and Quantitative CT-based Finite Element Analysis
Published on: March 11, 2017
Applying Machine Learning Analysis Based on Proximal Femur of Abdominal Computed Tomography to Screen for Abnormal
Xiaoqing Yuan1, Yanbo Liang1, Hui Yang1
1Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, 271000, China.
Rationale And Objectives:
To evaluate the performance of machine learning analysis based on proximal femur of abdominal computed tomography (CT) scans in screening for abnormal bone mass in femur.
Materials And Methods:
222 patients aged 50 years or older who underwent abdominal CT and dual-energy X-ray absorptiometry scans within 14 days were retrospectively enrolled. The patients were randomly assigned to a training cohort (n = 155) and a testing cohort (n = 67) in a ratio of 7:3. A total of 2288 candidate radiomic features were extracted from the volume region of interest - the left proximal femur of the abdominal CT scans. The most valuable radiomic features were selected using minimum-Redundancy Maximum-Relevancy and the least absolute shrinkage and selection operator to construct the radiomics model. The predictive performance was assessed with receiver operating characteristic curve.
Results:
13 features were chosen to establish the radiomics model. The radiomics model using logistic regression displayed excellent prediction performance in distinguishing normal bone mass and abnormal bone mass, with the area under the curve (AUC), accuracy, sensitivity and specificity of 0.917 (95% CI, 0.867-0.967), 0.826, 0.935 and 0.780 in the training cohort. The testing cohort indicated a better performance with AUC, accuracy, sensitivity and specificity of 0.963 (95% CI, 0.919-0.999), 0.851, 0.923 and 0.889.
Conclusion:
The radiomics model based on proximal femur of abdominal CT scans had a high predictive performance to identify abnormal bone mass in femur, which can be used as a tool for opportunistic osteoporosis screening.
