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 quan...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.展开更多
目的探讨液体衰减反转恢复(FLAIR)序列血管高信号(FVH)-DWI不匹配预测卒中血管再通治疗后预后的价值。方法前瞻性纳入2017年1月至2018年12月急性脑卒中接受血管再通治疗患者59例。所有患者发病时间均为6 h内,且于治疗前接受MRI检查。搜...目的探讨液体衰减反转恢复(FLAIR)序列血管高信号(FVH)-DWI不匹配预测卒中血管再通治疗后预后的价值。方法前瞻性纳入2017年1月至2018年12月急性脑卒中接受血管再通治疗患者59例。所有患者发病时间均为6 h内,且于治疗前接受MRI检查。搜集所有患者的FVH-DWI不匹配、DWI体积、3个月功能预后(mRS评分)及一般临床资料等。统计分析FVH-DWI不匹配与FVH-DWI匹配各参数之间的差异,应用多元逻辑回归分析预测卒中预后的独立预测因子。结果FVH-DWI不匹配组(39例)较无FVH-DWI不匹配组(20例)FVH评分更高(4.44±1.22 vs 3.55±1.99;P=0.038)、DWI体积较小(15.75±21.25 vs 48.71±47.86;P=0.007)、ASITN等级更高(2.64±0.67 vs 2.05±1.15;P=0.043)、ASITN间隔时间更长(5.49±1.96 vs 4.19±2.27;P=0.038)。FVH-DWI不匹配组3个月预后好于无FVH-DWI不匹配组(2.05±0.92 vs 2.90±1.52),差异有统计学意义(P=0.010)。多元逻辑回归分析显示DWI体积[(OR(95%CI):1.031(1.005~1.058);P=0.021]和FVH-DWI不匹配[(OR(95%CI):14.311(2.670~76.703);P=0.002]是预测预后的独立预测因子。结论FVH-DWI不匹配患者通常具有较小的DWI体积及较好的功能预后,FVH-DWI不匹配可以有效地预测卒中的功能预后、指导治疗。展开更多
文摘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.
文摘目的探讨液体衰减反转恢复(FLAIR)序列血管高信号(FVH)-DWI不匹配预测卒中血管再通治疗后预后的价值。方法前瞻性纳入2017年1月至2018年12月急性脑卒中接受血管再通治疗患者59例。所有患者发病时间均为6 h内,且于治疗前接受MRI检查。搜集所有患者的FVH-DWI不匹配、DWI体积、3个月功能预后(mRS评分)及一般临床资料等。统计分析FVH-DWI不匹配与FVH-DWI匹配各参数之间的差异,应用多元逻辑回归分析预测卒中预后的独立预测因子。结果FVH-DWI不匹配组(39例)较无FVH-DWI不匹配组(20例)FVH评分更高(4.44±1.22 vs 3.55±1.99;P=0.038)、DWI体积较小(15.75±21.25 vs 48.71±47.86;P=0.007)、ASITN等级更高(2.64±0.67 vs 2.05±1.15;P=0.043)、ASITN间隔时间更长(5.49±1.96 vs 4.19±2.27;P=0.038)。FVH-DWI不匹配组3个月预后好于无FVH-DWI不匹配组(2.05±0.92 vs 2.90±1.52),差异有统计学意义(P=0.010)。多元逻辑回归分析显示DWI体积[(OR(95%CI):1.031(1.005~1.058);P=0.021]和FVH-DWI不匹配[(OR(95%CI):14.311(2.670~76.703);P=0.002]是预测预后的独立预测因子。结论FVH-DWI不匹配患者通常具有较小的DWI体积及较好的功能预后,FVH-DWI不匹配可以有效地预测卒中的功能预后、指导治疗。
