目的:研究可预测PD-1/PD-L1抑制剂治疗恶性肿瘤临床疗效的潜在生物标志物。方法:检索PubMed、Web of Science、CNKI、万方和维普数据库,检索时限为各数据库建库至2022年9月20日。由2名评价员独立筛选文献、提取资料并评价纳入研究的偏...目的:研究可预测PD-1/PD-L1抑制剂治疗恶性肿瘤临床疗效的潜在生物标志物。方法:检索PubMed、Web of Science、CNKI、万方和维普数据库,检索时限为各数据库建库至2022年9月20日。由2名评价员独立筛选文献、提取资料并评价纳入研究的偏倚风险后,采用RevMan5.4和STATA16.0软件进行Meta分析。结果:共纳入18项研究,共计4018例患者。在随访的1年和2年内,发现高水平肿瘤突变负担(TMB)的肿瘤患者使用PD-1/PD-L1抑制剂的总生存率(OS)(P=0.003,P=0.01)和无进展生存率(PFS)(P=0.0002,P=0.04)更高。在不同的随访时间内,以1%为临界值,PD-L1表达高低作为预测PD-1/PD-L1抑制剂OS和PFS的生物标志物差异无统计学意义(P>0.05)。结论:TMB可以作为预测PD-1/PD-L1抑制剂治疗恶性肿瘤患者后2年内临床疗效的生物学指标,但其效用能否持续更长时间有待进一步研究;PD-L1单项检测目前不能成为预测应用PD-1/PD-L1抑制剂受益与否的生物学标志物。展开更多
Objective To explore the value of deep learning(DL)models semi-automatic training system for automatic optimization of clinical image quality control of transthoracic echocardiography(TTE).Methods Totally 1250 TTE vid...Objective To explore the value of deep learning(DL)models semi-automatic training system for automatic optimization of clinical image quality control of transthoracic echocardiography(TTE).Methods Totally 1250 TTE videos from 402 patients were retrospectively collected,including 490 apical four chamber(A4C),310 parasternal long axis view of left ventricle(PLAX)and 450 parasternal short axis view of great vessel(PSAX GV).The videos were divided into development set(245 A4C,155 PLAX,225 PSAX GV),semi-automated training set(98 A4C,62 PLAX,90 PSAX GV)and test set(147 A4C,93 PLAX,135 PSAX GV)at the ratio of 5∶2∶3.Based on development set and semi-automatic training set,DL model of quality control was semi-automatically iteratively optimized,and a semi-automatic training system was constructed,then the efficacy of DL models for recognizing TTE views and assessing imaging quality of TTE were verified in test set.Results After optimization,the overall accuracy,precision,recall,and F1 score of DL models for recognizing TTE views in test set improved from 97.33%,97.26%,97.26%and 97.26%to 99.73%,99.65%,99.77%and 99.71%,respectively,while the overall accuracy for assessing A4C,PLAX and PSAX GV TTE as standard views in test set improved from 89.12%,83.87%and 90.37%to 93.20%,90.32%and 93.33%,respectively.Conclusion The developed DL models semi-automatic training system could improve the efficiency of clinical imaging quality control of TTE and increase iteration speed.展开更多
文摘目的:研究可预测PD-1/PD-L1抑制剂治疗恶性肿瘤临床疗效的潜在生物标志物。方法:检索PubMed、Web of Science、CNKI、万方和维普数据库,检索时限为各数据库建库至2022年9月20日。由2名评价员独立筛选文献、提取资料并评价纳入研究的偏倚风险后,采用RevMan5.4和STATA16.0软件进行Meta分析。结果:共纳入18项研究,共计4018例患者。在随访的1年和2年内,发现高水平肿瘤突变负担(TMB)的肿瘤患者使用PD-1/PD-L1抑制剂的总生存率(OS)(P=0.003,P=0.01)和无进展生存率(PFS)(P=0.0002,P=0.04)更高。在不同的随访时间内,以1%为临界值,PD-L1表达高低作为预测PD-1/PD-L1抑制剂OS和PFS的生物标志物差异无统计学意义(P>0.05)。结论:TMB可以作为预测PD-1/PD-L1抑制剂治疗恶性肿瘤患者后2年内临床疗效的生物学指标,但其效用能否持续更长时间有待进一步研究;PD-L1单项检测目前不能成为预测应用PD-1/PD-L1抑制剂受益与否的生物学标志物。
文摘Objective To explore the value of deep learning(DL)models semi-automatic training system for automatic optimization of clinical image quality control of transthoracic echocardiography(TTE).Methods Totally 1250 TTE videos from 402 patients were retrospectively collected,including 490 apical four chamber(A4C),310 parasternal long axis view of left ventricle(PLAX)and 450 parasternal short axis view of great vessel(PSAX GV).The videos were divided into development set(245 A4C,155 PLAX,225 PSAX GV),semi-automated training set(98 A4C,62 PLAX,90 PSAX GV)and test set(147 A4C,93 PLAX,135 PSAX GV)at the ratio of 5∶2∶3.Based on development set and semi-automatic training set,DL model of quality control was semi-automatically iteratively optimized,and a semi-automatic training system was constructed,then the efficacy of DL models for recognizing TTE views and assessing imaging quality of TTE were verified in test set.Results After optimization,the overall accuracy,precision,recall,and F1 score of DL models for recognizing TTE views in test set improved from 97.33%,97.26%,97.26%and 97.26%to 99.73%,99.65%,99.77%and 99.71%,respectively,while the overall accuracy for assessing A4C,PLAX and PSAX GV TTE as standard views in test set improved from 89.12%,83.87%and 90.37%to 93.20%,90.32%and 93.33%,respectively.Conclusion The developed DL models semi-automatic training system could improve the efficiency of clinical imaging quality control of TTE and increase iteration speed.
