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Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and 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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228483/ https://www.ncbi.nlm.nih.gov/pubmed/37101352 http://dx.doi.org/10.1097/CM9.0000000000002353 |
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author | Guo, Lanwei Yu, Yue Yang, Funa Gao, Wendong Wang, Yu Xiao, Yao Du, Jia Tian, Jinhui Yang, Haiyan |
author_facet | Guo, Lanwei Yu, Yue Yang, Funa Gao, Wendong Wang, Yu Xiao, Yao Du, Jia Tian, Jinhui Yang, Haiyan |
author_sort | Guo, Lanwei |
collection | PubMed |
description | 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 I(2) statistic, and publication bias was evaluated using a Deeks’ funnel plot and linear regression test. RESULTS: A total of 49 studies with 157,762 individuals were identified for the final qualitative synthesis; most of them were from Europe and America (38 studies), ten were from Asia, and one was from Oceania. The recruitment period was 1992 to 2018, and most of the subjects were 40 to 75 years old. The analysis showed that the AUC of lung cancer screening by LDCT was 0.98 (95% CI: 0.96–0.99), and the overall sensitivity and specificity were 0.97 (95% CI: 0.94–0.98) and 0.87 (95% CI: 0.82–0.91), respectively. The funnel plot and test results showed that there was no significant publication bias among the included studies. CONCLUSIONS: Baseline LDCT has high sensitivity and specificity as a screening technique for lung cancer. However, long-term follow-up of the whole study population (including those with a negative baseline screening result) should be performed to enhance the accuracy of LDCT screening. |
format | Online Article Text |
id | pubmed-10228483 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-102284832023-05-31 Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis Guo, Lanwei Yu, Yue Yang, Funa Gao, Wendong Wang, Yu Xiao, Yao Du, Jia Tian, Jinhui Yang, Haiyan Chin Med J (Engl) 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 I(2) statistic, and publication bias was evaluated using a Deeks’ funnel plot and linear regression test. RESULTS: A total of 49 studies with 157,762 individuals were identified for the final qualitative synthesis; most of them were from Europe and America (38 studies), ten were from Asia, and one was from Oceania. The recruitment period was 1992 to 2018, and most of the subjects were 40 to 75 years old. The analysis showed that the AUC of lung cancer screening by LDCT was 0.98 (95% CI: 0.96–0.99), and the overall sensitivity and specificity were 0.97 (95% CI: 0.94–0.98) and 0.87 (95% CI: 0.82–0.91), respectively. The funnel plot and test results showed that there was no significant publication bias among the included studies. CONCLUSIONS: Baseline LDCT has high sensitivity and specificity as a screening technique for lung cancer. However, long-term follow-up of the whole study population (including those with a negative baseline screening result) should be performed to enhance the accuracy of LDCT screening. Lippincott Williams & Wilkins 2023-05-05 2023-04-27 /pmc/articles/PMC10228483/ /pubmed/37101352 http://dx.doi.org/10.1097/CM9.0000000000002353 Text en Copyright © 2023 The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
spellingShingle | Meta Analysis Guo, Lanwei Yu, Yue Yang, Funa Gao, Wendong Wang, Yu Xiao, Yao Du, Jia Tian, Jinhui Yang, Haiyan Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis |
title | Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis |
title_full | Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis |
title_fullStr | Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis |
title_full_unstemmed | Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis |
title_short | Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis |
title_sort | accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis |
topic | Meta Analysis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228483/ https://www.ncbi.nlm.nih.gov/pubmed/37101352 http://dx.doi.org/10.1097/CM9.0000000000002353 |
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