Unveiling Osteoporosis Through Radiomics Analysis of Hip CT Imaging.

RATIONALE AND OBJECTIVES

This study aims to investigate the use of radiomics analysis of hip CT imaging to unveil osteoporosis.

MATERIALS AND METHODS

The researchers analyzed hip CT scans from a cohort of patients, including both osteoporotic and healthy individuals. Radiomics technique are employed to extract a comprehensive array of features from these images, encompassing texture, shape, and intensity alterations. Radiomics analysis using the 10 most commonly used machine learning models was employed to identify screened radiomics features for the detection of osteoporosis in patients. In addition to radiomics features, the basic information of patients is also utilized as training data for these machine learning models to accurately identify the presence of osteoporosis. A comparison would be made between the efficiency of recognizing radiomics features and the efficiency of recognizing patient basic information. The machine learning model that achieves the highest performance would be chosen to integrate patient basic information and radiomics features for the development of clinical nomograms.

RESULT

After a thorough screening process, 16 radiomics features were selected as input parameters for the machine learning model. In the test group, the highest accuracy achieved using radiomics features was 0.849, with an area under the curve (AUC) of 0.919. Evaluation of clinical features identified age and gender as closely associated with osteoporosis. Among these features, the KNN model exhibited the highest accuracy of 0.731 and an AUC of 0.658 in the test group. Comparing the performance of radiomics and clinical features, radiomics features demonstrated superior AUC values in the machine learning models. Ultimately, the XGBoost model, utilizing both radiomics and clinical features, was selected as the final Nomogram prediction model. In the test group, this model achieved an accuracy of 0.882 and an AUC of 0.886 in screening for osteoporosis.

CONCLUSION

Radiomics features derived from hip CT scans exhibit strong screening capabilities for osteoporosis. Furthermore, when combined with easily obtainable clinical features like patient age and gender, an effective screening efficacy for osteoporosis can be achieved.