001     180644
005     20240229145626.0
024 7 _ |a 10.1002/ijc.34167
|2 doi
024 7 _ |a pmid:35809038
|2 pmid
024 7 _ |a 0020-7136
|2 ISSN
024 7 _ |a 1097-0215
|2 ISSN
024 7 _ |a altmetric:130910545
|2 altmetric
037 _ _ |a DKFZ-2022-01449
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Li, Mengmeng
|0 0000-0001-6630-7468
|b 0
245 _ _ |a The influence of postscreening follow-up time and participant characteristics on estimates of overdiagnosis from lung cancer screening trials.
260 _ _ |a Bognor Regis
|c 2022
|b Wiley-Liss
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1663232208_7133
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a 2022 Nov 1;151(9):1491-1501
520 _ _ |a We aimed to explore the underlying reasons that estimates of overdiagnosis vary across and within low-dose computed tomography (LDCT) lung cancer screening trials. We conducted a systematic review to identify estimates of overdiagnosis from randomised controlled trials of LDCT screening. We then analysed the association of Ps (the excess incidence of lung cancer as a proportion of screen-detected cases) with postscreening follow-up time using a linear random effects meta-regression model. Separately, we analysed annual Ps estimates from the US National Lung Screening Trial (NLST) and German Lung Cancer Screening Intervention Trial (LUSI) using exponential decay models with asymptotes. We conducted stratified analyses to investigate participant characteristics associated with Ps using the extended follow-up data from NLST. Among 12 overdiagnosis estimates from 8 trials, the postscreening follow-up ranged from 3.8 to 9.3 years, and Ps ranged from -27.0% (ITALUNG, 8.3 years follow-up) to 67.2% (DLCST, 5.0 years follow-up). Across trials, 39.1% of the variation in Ps was explained by postscreening follow-up time. The annual changes in Ps were -3.5% and -3.9% in the NLST and LUSI trials, respectively. Ps was predicted to plateau at 2.2% for NLST and 9.2% for LUSI with hypothetical infinite follow-up. In NLST, Ps increased with age from -14.9% (55-59 years) to 21.7% (70-74 years), and time trends in Ps varied by histological type. The findings suggest that differences in postscreening follow-up time partially explain variation in overdiagnosis estimates across lung cancer screening trials. Estimates of overdiagnosis should be interpreted in the context of postscreening follow-up and population characteristics.
536 _ _ |a 313 - Krebsrisikofaktoren und Prävention (POF4-313)
|0 G:(DE-HGF)POF4-313
|c POF4-313
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 7 |a lung cancer screening
|2 Other
650 _ 7 |a overdiagnosis
|2 Other
650 _ 7 |a randomised controlled trial
|2 Other
700 1 _ |a Zhang, Li
|b 1
700 1 _ |a Charvat, Hadrien
|0 0000-0003-3624-1394
|b 2
700 1 _ |a Callister, Matthew E
|b 3
700 1 _ |a Sasieni, Peter
|b 4
700 1 _ |a Christodoulou, Evangelia
|0 P:(DE-He78)8da2eca0bc6341c8681c317fe2b8e27b
|b 5
|u dkfz
700 1 _ |a Kaaks, Rudolf
|0 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a
|b 6
|u dkfz
700 1 _ |a Johansson, Mattias
|b 7
700 1 _ |a Carvalho, Andre L
|b 8
700 1 _ |a Vaccarella, Salvatore
|b 9
700 1 _ |a Robbins, Hilary A
|0 0000-0001-6041-6866
|b 10
773 _ _ |a 10.1002/ijc.34167
|g p. ijc.34167
|0 PERI:(DE-600)1474822-8
|n 9
|p 1491-1501
|t International journal of cancer
|v 151
|y 2022
|x 0020-7136
909 C O |p VDB
|o oai:inrepo02.dkfz.de:180644
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 5
|6 P:(DE-He78)8da2eca0bc6341c8681c317fe2b8e27b
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 6
|6 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-313
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Krebsrisikofaktoren und Prävention
|x 0
914 1 _ |y 2022
915 _ _ |a DEAL Wiley
|0 StatID:(DE-HGF)3001
|2 StatID
|d 2021-02-04
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-02-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-02-04
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-02-04
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2022-11-25
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-25
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2022-11-25
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-25
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-25
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2022-11-25
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2022-11-25
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b INT J CANCER : 2021
|d 2022-11-25
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b INT J CANCER : 2021
|d 2022-11-25
920 1 _ |0 I:(DE-He78)C020-20160331
|k C020
|l C020 Epidemiologie von Krebs
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C020-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21