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Background::Screening using low-dose computed tomography (LDCT) is a more effective approach and has the potential to detect lung cancer more accurately. We aimed to conduct a meta-analysis to estimate the accuracy of population-based screening studies primarily assessing baseline LDCT screening for lung cancer.Methods::MEDLINE, Excerpta Medica Database, and Web of Science were searched for articles published up to April 10, 2022. According to the inclusion and exclusion criteria, the data of true positives, false-positives, false negatives, and true negatives in the screening test were extracted. Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate the quality of the literature. A bivariate random effects model was used to estimate pooled sensitivity and specificity. The area under the curve (AUC) was calculated by using hierarchical summary receiver-operating characteristics analysis. Heterogeneity between studies was measured using the Higgins I2 statistic, and publication bias w

作者:Guo Lanwei;Yu Yue;Yang Funa;Gao Wendong;Wang Yu;Xiao Yao;Du Jia;Tian Jinhui;Yang Haiyan

来源:中华医学杂志英文版 2023 年 136卷 9期

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作者:
Guo Lanwei;Yu Yue;Yang Funa;Gao Wendong;Wang Yu;Xiao Yao;Du Jia;Tian Jinhui;Yang Haiyan
来源:
中华医学杂志英文版 2023 年 136卷 9期
标签:
Lung cancer Low-dose computed tomography Screening Sensitivity Specificity Meta-analysis Lung cancer Low-dose computed tomography Screening Sensitivity Specificity Meta-analysis
Background::Screening using low-dose computed tomography (LDCT) is a more effective approach and has the potential to detect lung cancer more accurately. We aimed to conduct a meta-analysis to estimate the accuracy of population-based screening studies primarily assessing baseline LDCT screening for lung cancer.Methods::MEDLINE, Excerpta Medica Database, and Web of Science were searched for articles published up to April 10, 2022. According to the inclusion and exclusion criteria, the data of true positives, false-positives, false negatives, and true negatives in the screening test were extracted. Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate the quality of the literature. A bivariate random effects model was used to estimate pooled sensitivity and specificity. The area under the curve (AUC) was calculated by using hierarchical summary receiver-operating characteristics analysis. Heterogeneity between studies was measured using the Higgins I2 statistic, and publication bias w