Utilizing radiomics techniques to isolate a single vertebral body from chest CT for opportunistic osteoporosis screening

作者：Xiaocong Lin, Rongkai Shen, Xiaoling Zheng, Shi Shaojian, Zhangsheng Dai, Kaibin Fang · 发表于：BMC Musculoskeletal Disorders · 年份：2024 · DOI：10.1186/s12891-024-07903-2 · 被引用次数：10 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Bone health and osteoporosis research、Bone and Joint Diseases

PURPOSE: Opportunistic osteoporosis screening, conducted during routine medical examinations such as chest computed tomography (CT), presents a potential solution for early detection. This study aims to investigate the feasibility of utilizing radiomics technology based on chest CT images to screen for opportunistic osteoporosis. METHODS: This Study is a Multicenter Retrospective Investigation. Relevant clinical data, including demographics and DXA results, would be collected for each participant. The radiomics analysis in this study focuses on the extraction of features from the 11th or 12th thoracic vertebral bodies from chest CT images. SVM machine learning models would be trained using these radiomic features, with DXA results as the ground truth for osteoporosis classification. RESULTS: In the training group, Clinical models had an accuracy of 0.684 and an AUC of 0.744, Radiomics models had an accuracy of 0.828 and an AUC of 0.896, Nomogram models had an accuracy of 0.839 and an AUC of 0.901. In the internal validation group, Clinical models had an accuracy of 0.769 and an AUC of 0.829, Radiomics models had an accuracy of 0.832 and an AUC of 0.892, Nomogram models had an accuracy of 0.839 and an AUC of 0.918. In the external validation group, Clinical models had an accuracy of 0.715 and an AUC of 0.741, Radiomics models had an accuracy of 0.777 and an AUC of 0.796, Nomogram models had an accuracy of 0.785 and an AUC of 0.807. In all three datasets, the Nomogram model exh...