Department of Spine Osteopathia, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China.
Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530021, People's Republic of China.
J Bone Miner Metab. 2023 Nov;41(6):877-889. doi: 10.1007/s00774-023-01469-0. Epub 2023 Oct 28.
The aim of this analysis is to construct a combined model that integrates radiomics, clinical risk factors, and machine learning algorithms to diagnose osteoporosis in patients and explore its potential in clinical applications.
A retrospective analysis was conducted on 616 lumbar spine. Radiomics features were extracted from the computed tomography (CT) scans and anteroposterior and lateral X-ray images of the lumbar spine. Logistic regression (LR), support vector machine (SVM), and random forest (RF) algorithms were used to construct radiomics models. The receiver operating characteristic curve (ROC) was employed to select the best-performing model. Clinical risk factors were identified through univariate logistic regression analysis (ULRA) and multivariate logistic regression analysis (MLRA) and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis (DCA).
A total of 4858 radiomics features were extracted. Among the radiomics models, the SVM model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.958 (0.9405-0.9762) in the training cohort and 0.907 (0.8648-0.9492) in the test cohort. Furthermore, the combined model exhibited an AUC of 0.959 (0.9412-0.9763) in the training cohort and 0.910 (0.8690-0.9506) in the test cohort.
The combined model displayed outstanding ability in diagnosing osteoporosis, providing a safe and efficient method for clinical decision-making.
本分析旨在构建一个结合放射组学、临床危险因素和机器学习算法的综合模型，用于诊断患者的骨质疏松症，并探讨其在临床应用中的潜力。
回顾性分析了 616 例腰椎。从腰椎 CT 扫描和前后位、侧位 X 线片提取放射组学特征。采用逻辑回归（LR）、支持向量机（SVM）和随机森林（RF）算法构建放射组学模型。采用受试者工作特征曲线（ROC）选择性能最佳的模型。通过单因素逻辑回归分析（ULRA）和多因素逻辑回归分析（MLRA）确定临床危险因素，并用于构建临床模型。然后通过合并放射组学和临床危险因素构建联合模型。采用 ROC 曲线分析评估模型性能，采用决策曲线分析（DCA）评估模型的临床价值。
共提取 4858 个放射组学特征。在放射组学模型中，SVM 模型表现出最佳的诊断能力和准确性，其在训练队列中的 AUC 为 0.958（0.9405-0.9762），在测试队列中的 AUC 为 0.907（0.8648-0.9492）。此外，联合模型在训练队列中的 AUC 为 0.959（0.9412-0.9763），在测试队列中的 AUC 为 0.910（0.8690-0.9506）。
联合模型在诊断骨质疏松症方面表现出卓越的能力，为临床决策提供了一种安全有效的方法。
