Objective:To develop a deep learning model to predict lymph node(LN)status in clinical stage IA lung adeno-carcinoma patients.Methods:This diagnostic study included 1,009 patients with pathologically confirmed clinica...Objective:To develop a deep learning model to predict lymph node(LN)status in clinical stage IA lung adeno-carcinoma patients.Methods:This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets(699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital)between January 2005 and December 2019.The Cancer Hospital dataset was randomly split into a training cohort(559 patients)and a validation cohort(140 patients)to train and tune a deep learning model based on a deep residual network(ResNet).The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model.Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography(HRCT)features for the model.The predictive performance was assessed by area under the curves(AUCs),accuracy,precision,recall,and F1 score.Subgroup analysis was performed to evaluate the potential bias of the study population.Results:A total of 1,009 patients were included in this study;409(40.5%)were male and 600(59.5%)were female.The median age was 57.0 years(inter-quartile range,IQR:50.0-64.0).The deep learning model achieved AUCs of 0.906(95%CI:0.873-0.938)and 0.893(95%CI:0.857-0.930)for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule(non-pGGN)testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.622).The precisions of this model for predicting pN0 disease were 0.979(95%CI:0.963-0.995)and 0.983(95%CI:0.967-0.998)in the testing cohort and the non-pGGN testing cohort,respectively.The deep learning model achieved AUCs of 0.848(95%CI:0.798-0.898)and 0.831(95%CI:0.776-0.887)for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.657).The recalls of this model for predicting pN2 disease were 0.903(95%CI:0.870-0.936)and 0.931(95%CI:0.901-0.961)in the testing cohort and the non-pGGN testing cohort,respectively.Conclusions:The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.展开更多
LEAFM ANIA的第二家店铺位于东京台东区(Taito Ward)CABO综合大楼的一层和二层。CABO综合大楼位于Yoyogi-Uehara车站旁,2023年刚建成,是一个集合了办公、住宅和娱乐设施于一体的多功能综合性大楼。LEAFM AN IA兼具零售、画廊和茶室双重...LEAFM ANIA的第二家店铺位于东京台东区(Taito Ward)CABO综合大楼的一层和二层。CABO综合大楼位于Yoyogi-Uehara车站旁,2023年刚建成,是一个集合了办公、住宅和娱乐设施于一体的多功能综合性大楼。LEAFM AN IA兼具零售、画廊和茶室双重功能,2层的结构为其分区提供了有利的条件,将动与静、冷清与热闹、分散与聚集自然地区分开来。展开更多
以骨形态发生蛋白受体IA(bone morphogenetic protein receptor IA,BMPR-IA)基因为候选基因,采用PCR-SSCP技术检测该基因在高繁殖力绵羊品种(小尾寒羊、湖羊)以及低繁殖力绵羊品种(多赛特羊、特克塞尔羊、德国肉用美利奴羊)中的单核苷...以骨形态发生蛋白受体IA(bone morphogenetic protein receptor IA,BMPR-IA)基因为候选基因,采用PCR-SSCP技术检测该基因在高繁殖力绵羊品种(小尾寒羊、湖羊)以及低繁殖力绵羊品种(多赛特羊、特克塞尔羊、德国肉用美利奴羊)中的单核苷酸多态性,同时研究该基因对小尾寒羊高繁殖力的影响。结果表明:在小尾寒羊中检测到AA、AB两种基因型,在湖羊中只检测到一种基因型BB,而在低繁殖力的3个绵羊品种中只检测到一种基因型AA。统计结果表明:小尾寒羊A等位基因频率为0.964,B等位基因频率为0.036。测序结果表明:BB型与AA型相比有6处核苷酸发生了突变。独立性检验表明:小尾寒羊与低繁殖力绵羊品种间基因型分布差异不显著(P>0.05),而湖羊与小尾寒羊、低繁殖力绵羊品种间基因型分布差异极显著(P<0.001)。AB基因型小尾寒羊平均产羔数比AA基因型多0.15只,但差异不显著(P>0.05)。研究表明:BMPR-IA基因不是小尾寒羊和湖羊高繁殖力的主效基因。展开更多
Issuing government information is a major task of the government Website.So it′s important that the government Website is organized in accordance with the users′need and habit.In this article,the theory of Informati...Issuing government information is a major task of the government Website.So it′s important that the government Website is organized in accordance with the users′need and habit.In this article,the theory of Information Architecture (IA) is used to examine and evaluate some government Websites in an attempt to investigate some elements of government Websites such as the navigation system,information retrieval method and ways of information organizing and labeling.展开更多
基金supported by the National Key R&D Program of China(grant numbers:2020AAA0109504,2023YFC2415200)CAMS Innovation Fund for Medical Sciences(grant number:2021-I2M-C&T-B-061)+5 种基金Beijing Hope Run Special Fund of Cancer Foundation of China(grant number:LC2022A22)the National Natural Science Foundation of China(grant numbers:81971619,81971580,92259302,82372053,91959205,82361168664,82022036,81971776)Beijing Natural Sci-ence Foundation(grant number:Z20J00105)Key-Area Research and Development Program of Guangdong Province(grant number:2021B0101420005)Strategic Priority Research Program of Chinese Academy of Sciences(grant number:XDB38040200)the Youth In-novation Promotion Association CAS(grant number:Y2021049).
文摘Objective:To develop a deep learning model to predict lymph node(LN)status in clinical stage IA lung adeno-carcinoma patients.Methods:This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets(699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital)between January 2005 and December 2019.The Cancer Hospital dataset was randomly split into a training cohort(559 patients)and a validation cohort(140 patients)to train and tune a deep learning model based on a deep residual network(ResNet).The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model.Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography(HRCT)features for the model.The predictive performance was assessed by area under the curves(AUCs),accuracy,precision,recall,and F1 score.Subgroup analysis was performed to evaluate the potential bias of the study population.Results:A total of 1,009 patients were included in this study;409(40.5%)were male and 600(59.5%)were female.The median age was 57.0 years(inter-quartile range,IQR:50.0-64.0).The deep learning model achieved AUCs of 0.906(95%CI:0.873-0.938)and 0.893(95%CI:0.857-0.930)for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule(non-pGGN)testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.622).The precisions of this model for predicting pN0 disease were 0.979(95%CI:0.963-0.995)and 0.983(95%CI:0.967-0.998)in the testing cohort and the non-pGGN testing cohort,respectively.The deep learning model achieved AUCs of 0.848(95%CI:0.798-0.898)and 0.831(95%CI:0.776-0.887)for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.657).The recalls of this model for predicting pN2 disease were 0.903(95%CI:0.870-0.936)and 0.931(95%CI:0.901-0.961)in the testing cohort and the non-pGGN testing cohort,respectively.Conclusions:The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.
文摘LEAFM ANIA的第二家店铺位于东京台东区(Taito Ward)CABO综合大楼的一层和二层。CABO综合大楼位于Yoyogi-Uehara车站旁,2023年刚建成,是一个集合了办公、住宅和娱乐设施于一体的多功能综合性大楼。LEAFM AN IA兼具零售、画廊和茶室双重功能,2层的结构为其分区提供了有利的条件,将动与静、冷清与热闹、分散与聚集自然地区分开来。
文摘以骨形态发生蛋白受体IA(bone morphogenetic protein receptor IA,BMPR-IA)基因为候选基因,采用PCR-SSCP技术检测该基因在高繁殖力绵羊品种(小尾寒羊、湖羊)以及低繁殖力绵羊品种(多赛特羊、特克塞尔羊、德国肉用美利奴羊)中的单核苷酸多态性,同时研究该基因对小尾寒羊高繁殖力的影响。结果表明:在小尾寒羊中检测到AA、AB两种基因型,在湖羊中只检测到一种基因型BB,而在低繁殖力的3个绵羊品种中只检测到一种基因型AA。统计结果表明:小尾寒羊A等位基因频率为0.964,B等位基因频率为0.036。测序结果表明:BB型与AA型相比有6处核苷酸发生了突变。独立性检验表明:小尾寒羊与低繁殖力绵羊品种间基因型分布差异不显著(P>0.05),而湖羊与小尾寒羊、低繁殖力绵羊品种间基因型分布差异极显著(P<0.001)。AB基因型小尾寒羊平均产羔数比AA基因型多0.15只,但差异不显著(P>0.05)。研究表明:BMPR-IA基因不是小尾寒羊和湖羊高繁殖力的主效基因。
文摘Issuing government information is a major task of the government Website.So it′s important that the government Website is organized in accordance with the users′need and habit.In this article,the theory of Information Architecture (IA) is used to examine and evaluate some government Websites in an attempt to investigate some elements of government Websites such as the navigation system,information retrieval method and ways of information organizing and labeling.
