Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
J Med Internet Res. 2024 Jul 12;26:e48535. doi: 10.2196/48535.
With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations.
The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles.
The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles.
The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001).
The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.
随着人口老龄化的逐渐增加，机会性计算机断层扫描（CT）的使用也在增加，这可能是获取老龄化人群骨骼和肌肉信息的一种有价值的方法。
本研究旨在通过使用椎体骨和椎旁肌肉的 CT 图像开发和外部验证机会性 CT 骨折预测模型。
该模型是基于 2010 年至 2019 年之间进行的腹部 CT 图像的回顾性纵向队列研究而建立的。该模型在 495 名患者中进行了外部验证。本研究的主要结果定义为在 5 年随访期间识别椎体骨折事件的预测准确性。图像模型是使用椎体骨和椎旁肌肉的图像通过注意力卷积神经网络-递归神经网络模型进行开发的。
在开发和验证组中，患者的平均年龄分别为 73 岁和 68 岁，69.1%（839/1214）和 78.8%（390/495）为女性。在外部验证队列中，椎体骨和椎旁肌肉图像预测椎体骨折的受试者工作特征曲线下面积（AUROC）优于仅骨图像（0.827，95%CI 0.821-0.833 与 0.815，95%CI 0.806-0.824，分别；P<.001）。这些图像模型的 AUROC 高于骨折风险评估模型（主要骨质疏松风险为 0.810，髋部骨折风险为 0.780）。对于使用年龄、性别、BMI、使用类固醇、吸烟、可能的继发性骨质疏松症、2 型糖尿病、HIV、丙型肝炎和肾衰竭的临床模型，外部验证队列中的 AUROC 值为 0.749（95%CI 0.736-0.762），低于使用椎体骨和肌肉的图像模型（P<.001）。
使用椎体骨和椎旁肌肉图像的模型表现优于仅使用骨图像或临床变量的模型。机会性 CT 筛查可能有助于未来识别高骨折风险的患者。
