基于 CT 成像对椎旁肌肉的研究，放射组学在预测 2 型糖尿病患者异常骨量方面的价值。
The value of radiomics to predict abnormal bone mass in type 2 diabetes mellitus patients based on CT imaging for paravertebral muscles.
机构信息
Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, China.
GE Healthcare, Precision Health Institution, Shanghai, China.
出版信息
Front Endocrinol (Lausanne). 2022 Oct 13;13:963246. doi: 10.3389/fendo.2022.963246. eCollection 2022.
OBJECTIVE
To investigate the value of CT imaging features of paravertebral muscles in predicting abnormal bone mass in patients with type 2 diabetes mellitus.
METHODS
The clinical and QCT data of 149 patients with type 2 diabetes mellitus were collected retrospectively. Patients were randomly divided into the training group (n = 90) and the validation group (n = 49). The radiologic model and Nomogram model were established by multivariate Logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) curves.
RESULTS
A total of 829 features were extracted from CT images of paravertebral muscles, and 12 optimal predictive features were obtained by the mRMR and Lasso feature selection methods. The radiomics model can better predict bone abnormality in type 2 diabetes mellitus, and the (Area Under Curve) AUC values of the training group and the validation group were 0.94(95% CI, 0.90-0.99) and 0.90(95% CI, 0.82-0.98). The combined Nomogram model, based on radiomics and clinical characteristics (vertebral CT values), showed better predictive efficacy with an AUC values of 0.97(95% CI, 0.94-1.00) in the training group and 0.95(95% CI, 0.90-1.00) in the validation group, compared with the clinical model.
CONCLUSION
The combination of Nomogram model and radiomics-clinical features of paravertebral muscles has a good predictive value for abnormal bone mass in patients with type 2 diabetes mellitus.
目的
探讨 2 型糖尿病患者椎旁肌 CT 影像特征对骨量异常的预测价值。
方法
回顾性收集 149 例 2 型糖尿病患者的临床和 QCT 资料，将患者随机分为训练组（n=90）和验证组（n=49）。采用多因素 Logistic 回归分析建立放射模型和 Nomogram 模型，通过受试者工作特征（ROC）曲线评估预测性能。
结果
从椎旁肌 CT 图像中提取了 829 个特征，通过 mRMR 和 Lasso 特征选择方法得到了 12 个最佳预测特征。放射组学模型能更好地预测 2 型糖尿病患者的骨异常，训练组和验证组的（曲线下面积）AUC 值分别为 0.94（95%CI，0.90-0.99）和 0.90（95%CI，0.82-0.98）。基于放射组学和临床特征（椎体 CT 值）的联合 Nomogram 模型显示出更好的预测效能，在训练组和验证组的 AUC 值分别为 0.97（95%CI，0.94-1.00）和 0.95（95%CI，0.90-1.00），优于临床模型。
结论
椎旁肌的 Nomogram 模型联合放射组学-临床特征对 2 型糖尿病患者骨量异常具有良好的预测价值。
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