目的:评价基于CT血管成像(CTA)和数字减影血管造影(DSA)的膈下动脉(IPA)解剖学变异的发生率与临床相关性。方法:系统检索PubMed、Web of Science、Scopus、Embase、Google Scholar、CBM、CNKI、WanFang、VIP和Baidu Scholar等数据库,纳...目的:评价基于CT血管成像(CTA)和数字减影血管造影(DSA)的膈下动脉(IPA)解剖学变异的发生率与临床相关性。方法:系统检索PubMed、Web of Science、Scopus、Embase、Google Scholar、CBM、CNKI、WanFang、VIP和Baidu Scholar等数据库,纳入与左、右膈下动脉(LIPA和RIPA)相关的文献,并采用Stata 17.0软件进行Meta分析。结果:共纳入19篇文献,包括6754例患者。IPA共干和单独起源的汇总发生率分别为29.4%(95%CI:24.8%~34.2%)和70.6%(95%CI:65.8%~75.2%);IPA共干以起源于腹主动脉和腹腔干最为常见,汇总发生率分别为48.2%(95%CI:42.4%~54.1%)和49.0%(95%CI:43.3%~54.7%);RIPA单独起源以腹主动脉和腹腔干最为常见,汇总发生率分别为40.9%(95%CI:36.7%~45.3%)和34.8%(95%CI:30.7%~38.9%);LIPA单独起源以腹腔干和腹主动脉最为常见,汇总发生率分别为58.5%(95%CI:53.2%~63.7%)和32.6%(95%CI:27.3%~38.1%)。结论:IPA的起源变异十分丰富,熟悉IPA的起源变异对介入放射学、胃肠病学、外科学和创伤学医师具有十分重要的临床意义。展开更多
目的评价CTA和DSA对Bühler弓(arc of Bühler,AOB)的检出率和临床意义。方法检索PubMed、Web of Science、Scopus、Embase、Google Scholar、CBM、CNKI、WanFang、VIP、Baidu Scholar数据库,纳入AOB相关的文献,采用Stata 17.0...目的评价CTA和DSA对Bühler弓(arc of Bühler,AOB)的检出率和临床意义。方法检索PubMed、Web of Science、Scopus、Embase、Google Scholar、CBM、CNKI、WanFang、VIP、Baidu Scholar数据库,纳入AOB相关的文献,采用Stata 17.0软件进行Meta分析。结果共计纳入11篇文献,包括研究对象3837例(含65例AOB)。AOB的总检出率为1.9%(0.8%~3.2%),CTA显示AOB的总检出率为2.0%(0.5%~4.3%),DSA显示AOB的总检出率为1.8%(0.5%~3.9%)。结论AOB是一种罕见的解剖学变异,在实施相关腹部手术时应考虑到AOB的存在,以免造成操作困难、腹腔脏器缺血或出血等并发症。展开更多
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
文摘目的评价CTA和DSA对Bühler弓(arc of Bühler,AOB)的检出率和临床意义。方法检索PubMed、Web of Science、Scopus、Embase、Google Scholar、CBM、CNKI、WanFang、VIP、Baidu Scholar数据库,纳入AOB相关的文献,采用Stata 17.0软件进行Meta分析。结果共计纳入11篇文献,包括研究对象3837例(含65例AOB)。AOB的总检出率为1.9%(0.8%~3.2%),CTA显示AOB的总检出率为2.0%(0.5%~4.3%),DSA显示AOB的总检出率为1.8%(0.5%~3.9%)。结论AOB是一种罕见的解剖学变异,在实施相关腹部手术时应考虑到AOB的存在,以免造成操作困难、腹腔脏器缺血或出血等并发症。
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.
