目的:探讨机器学习结合CT影像组学特征构建模型预测2型糖尿病(type 2 diabetes mellitus,T2DM)患者椎体脆性骨折的准确性.方法:回顾性收集140例(新发椎体脆性骨折的T2DM患者70例,对照组70例)患者CT图像和临床资料.另收集18例(椎体脆性骨折的T2DM患者16例,对照组2例)患者的前次CT图像和临床资料作为外部验证集.应用单因素分析、Pear-son 相关分析、最小冗余度最大相关度算法、二元 logistic 回归分析和最小绝对值收缩和选择算子模型筛选出最佳特征.基于支持向量机、多层感知器、极端梯度提升(eXtreme Gradient Boosting,XGBoost)构建预测模型.应用受试者工作特征曲线下面积(area under the curve,AUC)对模型效能进行评估.结果:从每例患者的CT图像中提取了 1 037个影像组学特征,然后精简为14个影像组学特征.17个临床特征中性别、年龄、体质指数是预测结果的独立因素.其中XGBoost分类器表现最好,训练集中XGBoost模型的AUC分别为1.000、0.929、1.000;测试集中分别为0.954、0.862、0.969.结论:基于临床及影像组学特征构建的XGBoost模型可作为预测T2DM患者椎体脆性骨折的一种无创性辅助工具.
Machine learning combined with computed tomography radiomics in predicting vertebral fragility fractures in patients with type 2 diabetes mellitus
Objective:To investigate the accuracy of a model established based on machine learning and computed tomography(CT)radiomics features in predicting vertebral fragility fractures in patients with type 2 diabetes mellitus(T2DM).Methods:A retrospective analysis was performed for the CT images and clinical data of 140 patients,among whom there were 70 T2DM patients with newly diag-nosed vertebral fragility fractures and 70 patients in the control group.The previous CT images and clinical data of 18 patients(16 T2DM patients with vertebral fragility fractures and 2 patients in the control group)were collected as an external validation set.The optimal features were screened by the univariate analysis,the Pearson correlation analysis,minimum redundancy maximum relevance algorithm,the binary logistic regression analysis,and the least absolute shrinkage and selection operator regression model,and then a predictive model was constructed by support vector machine,multi-layer perceptron,and eXtreme gradient boosting(XGBoost)classi-fiers.The area under the ROC curve(AUC)was used to evaluate the predictive performance of the model.Results:A total of 1 037 radiomics features were extracted from the CT images of each patient and were then simplified into 14 radiomics features.Among the 17 clinical features,sex,age,and body mass index were independent factors for predicting outcome.XGBoost classifier showed the best performance,and the XGBoost model showed an AUC of 1.000,0.929,and 1.000,respectively,in the training set and an AUC of 0.954,0.862,and 0.969,respectively,in the test set.Conclusion:The XGBoost model based on clinical and radiomics features can be used as a noninvasive tool for predicting vertebral fragility fractures in T2DM patients.
万方数据
维普
