Artificial intelligence models based on non-contrast chest CT for measuring bone mineral density

基于胸部平扫CT人工智能模型测量骨密度

Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy bones of T5—T10 vertebrae were segmented as ROI,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In test set,AUC of ML Bagging-OP,Rad Bagging-OP and DL OP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P>0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of Ax-G±1.96 s),which were highly positively correlated(r=0.910—0.974,all P<0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.

目的观察基于胸部平扫CT人工智能(AI)模型测量骨密度(BMD)的价值。方法回顾性纳入先后接受胸部平扫及定量CT(QCT)检测BMD的体检者380人,按照8∶2比例将其分为训练集(n=304)与测试集(n=76)。采用QCT测量L1~L3椎体平均BMD,于胸部平扫CT中分割T5~T10椎体松质骨作为ROI,提取其影像组学(Rad)特征,用于构建分类诊断骨质疏松症(OP)及评估BMD的机器学习(ML)、Rad及深度学习(DL)模型;绘制受试者工作特征曲线,计算曲线下面积(AUC),评估各模型分类诊断OP的效能;分别以Pearson相关分析及Bland-Altman分析观察各模型与QCT测量BMD结果的一致性及其相关性。结果ML与Rad模型中,基于Bagging决策树算法模型(MLBagging-OP、RadBagging-OP)分类诊断OP效能最佳;MLBagging-OP、RadBagging-OP及DLOP模型分类诊断测试集OP的AUC分别为0.943、0.944及0.947,差异均无统计学意义(P均>0.05)。各模型与QCT测量BMD结果的一致性均良好(绝大多数差值位于±1.96s范围内),且均呈高度正相关(r=0.910~0.974,P均<0.001)。结论基于胸部平扫CT的AI模型分类诊断OP效能高,且所测BMD与QCT结果一致性良好。