Estimation of Lead Time via Low-Dose CT in the National Lung Screening Trial
Abstract Based on recent reports from the National Lung Screening Trial (NLST), smokers who were screened by low-dose computer tomography (LDCT) had a 20% lower chance of dying from lung cancer than those screened by chest X-rays. However, due to the complexities of lead time bias and over diagnosis...
Ausführliche Beschreibung
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| Autor*in: |
Liu, Ruiqi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer International Publishing AG, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of healthcare informatics research - [Cham] : Springer International Publishing, 2017, 2(2018), 4 vom: 12. Juni, Seite 353-366 |
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| Übergeordnetes Werk: |
volume:2 ; year:2018 ; number:4 ; day:12 ; month:06 ; pages:353-366 |
| Links: |
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| DOI / URN: |
10.1007/s41666-018-0027-8 |
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| Katalog-ID: |
SPR038281309 |
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| 520 | |a Abstract Based on recent reports from the National Lung Screening Trial (NLST), smokers who were screened by low-dose computer tomography (LDCT) had a 20% lower chance of dying from lung cancer than those screened by chest X-rays. However, due to the complexities of lead time bias and over diagnosis, no formal test has been shown to reduce lung cancer mortality. To correctly evaluate survival benefit due to early detection, it is critical to estimate lead time, the length of time that detection of a disease is advanced by screening. We applied a recently developed probability method to estimate lead time, using the LDCT data from NLST, where human lifetime was treated as random and derived from the actuarial life table of the US Social Security Administration. Using Bayesian posterior samples of key parameters extracted from the NLST-LDCT data, simulations on lead time were carried out on 16 hypothetical cohorts with four initial ages (55, 60, 65, and 70) and four future screening intervals (12, 18, 24, and 30 months). For each scenario, the estimated lead time for both screen-detected and interval cases is reported. Results show that the probability of no-early-detection (interval cases) increases monotonically when the screening interval increases for both genders. A male heavy smoker with an initial screening age at 60 has 11.65% (female 6.76%) chance of no-early-detection with annual screenings. This probability increases to 36.35% (female 28.26%) if the screenings were biennial. The mean lead time appears longer for women than for men. The mean lead time decreases when the screening interval increases, but it appears stable across different initial age groups. These results lay a foundation to evaluate survival benefit accurately with LDCT and to schedule future screening exams. | ||
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| 650 | 4 | |a Low-dose computed tomography |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Pérez, Adriana |4 aut | |
| 700 | 1 | |a Wu, Dongfeng |0 (orcid)0000-0002-6764-0083 |4 aut | |
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10.1007/s41666-018-0027-8 doi (DE-627)SPR038281309 (SPR)s41666-018-0027-8-e DE-627 ger DE-627 rakwb eng Liu, Ruiqi verfasserin aut Estimation of Lead Time via Low-Dose CT in the National Lung Screening Trial 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract Based on recent reports from the National Lung Screening Trial (NLST), smokers who were screened by low-dose computer tomography (LDCT) had a 20% lower chance of dying from lung cancer than those screened by chest X-rays. However, due to the complexities of lead time bias and over diagnosis, no formal test has been shown to reduce lung cancer mortality. To correctly evaluate survival benefit due to early detection, it is critical to estimate lead time, the length of time that detection of a disease is advanced by screening. We applied a recently developed probability method to estimate lead time, using the LDCT data from NLST, where human lifetime was treated as random and derived from the actuarial life table of the US Social Security Administration. Using Bayesian posterior samples of key parameters extracted from the NLST-LDCT data, simulations on lead time were carried out on 16 hypothetical cohorts with four initial ages (55, 60, 65, and 70) and four future screening intervals (12, 18, 24, and 30 months). For each scenario, the estimated lead time for both screen-detected and interval cases is reported. Results show that the probability of no-early-detection (interval cases) increases monotonically when the screening interval increases for both genders. A male heavy smoker with an initial screening age at 60 has 11.65% (female 6.76%) chance of no-early-detection with annual screenings. This probability increases to 36.35% (female 28.26%) if the screenings were biennial. The mean lead time appears longer for women than for men. The mean lead time decreases when the screening interval increases, but it appears stable across different initial age groups. These results lay a foundation to evaluate survival benefit accurately with LDCT and to schedule future screening exams. Cancer screening (dpeaa)DE-He213 National Lung Screening Trial (dpeaa)DE-He213 No-early-detection (dpeaa)DE-He213 Low-dose computed tomography (dpeaa)DE-He213 Pérez, Adriana aut Wu, Dongfeng (orcid)0000-0002-6764-0083 aut Enthalten in Journal of healthcare informatics research [Cham] : Springer International Publishing, 2017 2(2018), 4 vom: 12. Juni, Seite 353-366 (DE-627)888229208 (DE-600)2895595-X 2509-498X nnns volume:2 year:2018 number:4 day:12 month:06 pages:353-366 https://dx.doi.org/10.1007/s41666-018-0027-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 2 2018 4 12 06 353-366 |
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10.1007/s41666-018-0027-8 doi (DE-627)SPR038281309 (SPR)s41666-018-0027-8-e DE-627 ger DE-627 rakwb eng Liu, Ruiqi verfasserin aut Estimation of Lead Time via Low-Dose CT in the National Lung Screening Trial 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract Based on recent reports from the National Lung Screening Trial (NLST), smokers who were screened by low-dose computer tomography (LDCT) had a 20% lower chance of dying from lung cancer than those screened by chest X-rays. However, due to the complexities of lead time bias and over diagnosis, no formal test has been shown to reduce lung cancer mortality. To correctly evaluate survival benefit due to early detection, it is critical to estimate lead time, the length of time that detection of a disease is advanced by screening. We applied a recently developed probability method to estimate lead time, using the LDCT data from NLST, where human lifetime was treated as random and derived from the actuarial life table of the US Social Security Administration. Using Bayesian posterior samples of key parameters extracted from the NLST-LDCT data, simulations on lead time were carried out on 16 hypothetical cohorts with four initial ages (55, 60, 65, and 70) and four future screening intervals (12, 18, 24, and 30 months). For each scenario, the estimated lead time for both screen-detected and interval cases is reported. Results show that the probability of no-early-detection (interval cases) increases monotonically when the screening interval increases for both genders. A male heavy smoker with an initial screening age at 60 has 11.65% (female 6.76%) chance of no-early-detection with annual screenings. This probability increases to 36.35% (female 28.26%) if the screenings were biennial. The mean lead time appears longer for women than for men. The mean lead time decreases when the screening interval increases, but it appears stable across different initial age groups. These results lay a foundation to evaluate survival benefit accurately with LDCT and to schedule future screening exams. Cancer screening (dpeaa)DE-He213 National Lung Screening Trial (dpeaa)DE-He213 No-early-detection (dpeaa)DE-He213 Low-dose computed tomography (dpeaa)DE-He213 Pérez, Adriana aut Wu, Dongfeng (orcid)0000-0002-6764-0083 aut Enthalten in Journal of healthcare informatics research [Cham] : Springer International Publishing, 2017 2(2018), 4 vom: 12. Juni, Seite 353-366 (DE-627)888229208 (DE-600)2895595-X 2509-498X nnns volume:2 year:2018 number:4 day:12 month:06 pages:353-366 https://dx.doi.org/10.1007/s41666-018-0027-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 2 2018 4 12 06 353-366 |
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10.1007/s41666-018-0027-8 doi (DE-627)SPR038281309 (SPR)s41666-018-0027-8-e DE-627 ger DE-627 rakwb eng Liu, Ruiqi verfasserin aut Estimation of Lead Time via Low-Dose CT in the National Lung Screening Trial 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract Based on recent reports from the National Lung Screening Trial (NLST), smokers who were screened by low-dose computer tomography (LDCT) had a 20% lower chance of dying from lung cancer than those screened by chest X-rays. However, due to the complexities of lead time bias and over diagnosis, no formal test has been shown to reduce lung cancer mortality. To correctly evaluate survival benefit due to early detection, it is critical to estimate lead time, the length of time that detection of a disease is advanced by screening. We applied a recently developed probability method to estimate lead time, using the LDCT data from NLST, where human lifetime was treated as random and derived from the actuarial life table of the US Social Security Administration. Using Bayesian posterior samples of key parameters extracted from the NLST-LDCT data, simulations on lead time were carried out on 16 hypothetical cohorts with four initial ages (55, 60, 65, and 70) and four future screening intervals (12, 18, 24, and 30 months). For each scenario, the estimated lead time for both screen-detected and interval cases is reported. Results show that the probability of no-early-detection (interval cases) increases monotonically when the screening interval increases for both genders. A male heavy smoker with an initial screening age at 60 has 11.65% (female 6.76%) chance of no-early-detection with annual screenings. This probability increases to 36.35% (female 28.26%) if the screenings were biennial. The mean lead time appears longer for women than for men. The mean lead time decreases when the screening interval increases, but it appears stable across different initial age groups. These results lay a foundation to evaluate survival benefit accurately with LDCT and to schedule future screening exams. Cancer screening (dpeaa)DE-He213 National Lung Screening Trial (dpeaa)DE-He213 No-early-detection (dpeaa)DE-He213 Low-dose computed tomography (dpeaa)DE-He213 Pérez, Adriana aut Wu, Dongfeng (orcid)0000-0002-6764-0083 aut Enthalten in Journal of healthcare informatics research [Cham] : Springer International Publishing, 2017 2(2018), 4 vom: 12. Juni, Seite 353-366 (DE-627)888229208 (DE-600)2895595-X 2509-498X nnns volume:2 year:2018 number:4 day:12 month:06 pages:353-366 https://dx.doi.org/10.1007/s41666-018-0027-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 2 2018 4 12 06 353-366 |
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10.1007/s41666-018-0027-8 doi (DE-627)SPR038281309 (SPR)s41666-018-0027-8-e DE-627 ger DE-627 rakwb eng Liu, Ruiqi verfasserin aut Estimation of Lead Time via Low-Dose CT in the National Lung Screening Trial 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract Based on recent reports from the National Lung Screening Trial (NLST), smokers who were screened by low-dose computer tomography (LDCT) had a 20% lower chance of dying from lung cancer than those screened by chest X-rays. However, due to the complexities of lead time bias and over diagnosis, no formal test has been shown to reduce lung cancer mortality. To correctly evaluate survival benefit due to early detection, it is critical to estimate lead time, the length of time that detection of a disease is advanced by screening. We applied a recently developed probability method to estimate lead time, using the LDCT data from NLST, where human lifetime was treated as random and derived from the actuarial life table of the US Social Security Administration. Using Bayesian posterior samples of key parameters extracted from the NLST-LDCT data, simulations on lead time were carried out on 16 hypothetical cohorts with four initial ages (55, 60, 65, and 70) and four future screening intervals (12, 18, 24, and 30 months). For each scenario, the estimated lead time for both screen-detected and interval cases is reported. Results show that the probability of no-early-detection (interval cases) increases monotonically when the screening interval increases for both genders. A male heavy smoker with an initial screening age at 60 has 11.65% (female 6.76%) chance of no-early-detection with annual screenings. This probability increases to 36.35% (female 28.26%) if the screenings were biennial. The mean lead time appears longer for women than for men. The mean lead time decreases when the screening interval increases, but it appears stable across different initial age groups. These results lay a foundation to evaluate survival benefit accurately with LDCT and to schedule future screening exams. Cancer screening (dpeaa)DE-He213 National Lung Screening Trial (dpeaa)DE-He213 No-early-detection (dpeaa)DE-He213 Low-dose computed tomography (dpeaa)DE-He213 Pérez, Adriana aut Wu, Dongfeng (orcid)0000-0002-6764-0083 aut Enthalten in Journal of healthcare informatics research [Cham] : Springer International Publishing, 2017 2(2018), 4 vom: 12. Juni, Seite 353-366 (DE-627)888229208 (DE-600)2895595-X 2509-498X nnns volume:2 year:2018 number:4 day:12 month:06 pages:353-366 https://dx.doi.org/10.1007/s41666-018-0027-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 2 2018 4 12 06 353-366 |
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10.1007/s41666-018-0027-8 doi (DE-627)SPR038281309 (SPR)s41666-018-0027-8-e DE-627 ger DE-627 rakwb eng Liu, Ruiqi verfasserin aut Estimation of Lead Time via Low-Dose CT in the National Lung Screening Trial 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract Based on recent reports from the National Lung Screening Trial (NLST), smokers who were screened by low-dose computer tomography (LDCT) had a 20% lower chance of dying from lung cancer than those screened by chest X-rays. However, due to the complexities of lead time bias and over diagnosis, no formal test has been shown to reduce lung cancer mortality. To correctly evaluate survival benefit due to early detection, it is critical to estimate lead time, the length of time that detection of a disease is advanced by screening. We applied a recently developed probability method to estimate lead time, using the LDCT data from NLST, where human lifetime was treated as random and derived from the actuarial life table of the US Social Security Administration. Using Bayesian posterior samples of key parameters extracted from the NLST-LDCT data, simulations on lead time were carried out on 16 hypothetical cohorts with four initial ages (55, 60, 65, and 70) and four future screening intervals (12, 18, 24, and 30 months). For each scenario, the estimated lead time for both screen-detected and interval cases is reported. Results show that the probability of no-early-detection (interval cases) increases monotonically when the screening interval increases for both genders. A male heavy smoker with an initial screening age at 60 has 11.65% (female 6.76%) chance of no-early-detection with annual screenings. This probability increases to 36.35% (female 28.26%) if the screenings were biennial. The mean lead time appears longer for women than for men. The mean lead time decreases when the screening interval increases, but it appears stable across different initial age groups. These results lay a foundation to evaluate survival benefit accurately with LDCT and to schedule future screening exams. Cancer screening (dpeaa)DE-He213 National Lung Screening Trial (dpeaa)DE-He213 No-early-detection (dpeaa)DE-He213 Low-dose computed tomography (dpeaa)DE-He213 Pérez, Adriana aut Wu, Dongfeng (orcid)0000-0002-6764-0083 aut Enthalten in Journal of healthcare informatics research [Cham] : Springer International Publishing, 2017 2(2018), 4 vom: 12. Juni, Seite 353-366 (DE-627)888229208 (DE-600)2895595-X 2509-498X nnns volume:2 year:2018 number:4 day:12 month:06 pages:353-366 https://dx.doi.org/10.1007/s41666-018-0027-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 2 2018 4 12 06 353-366 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR038281309</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519195554.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s41666-018-0027-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR038281309</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s41666-018-0027-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liu, Ruiqi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Estimation of Lead Time via Low-Dose CT in the National Lung Screening Trial</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer International Publishing AG, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Based on recent reports from the National Lung Screening Trial (NLST), smokers who were screened by low-dose computer tomography (LDCT) had a 20% lower chance of dying from lung cancer than those screened by chest X-rays. However, due to the complexities of lead time bias and over diagnosis, no formal test has been shown to reduce lung cancer mortality. To correctly evaluate survival benefit due to early detection, it is critical to estimate lead time, the length of time that detection of a disease is advanced by screening. We applied a recently developed probability method to estimate lead time, using the LDCT data from NLST, where human lifetime was treated as random and derived from the actuarial life table of the US Social Security Administration. Using Bayesian posterior samples of key parameters extracted from the NLST-LDCT data, simulations on lead time were carried out on 16 hypothetical cohorts with four initial ages (55, 60, 65, and 70) and four future screening intervals (12, 18, 24, and 30 months). For each scenario, the estimated lead time for both screen-detected and interval cases is reported. Results show that the probability of no-early-detection (interval cases) increases monotonically when the screening interval increases for both genders. A male heavy smoker with an initial screening age at 60 has 11.65% (female 6.76%) chance of no-early-detection with annual screenings. This probability increases to 36.35% (female 28.26%) if the screenings were biennial. The mean lead time appears longer for women than for men. The mean lead time decreases when the screening interval increases, but it appears stable across different initial age groups. 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Liu, Ruiqi |
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estimation of lead time via low-dose ct in the national lung screening trial |
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Estimation of Lead Time via Low-Dose CT in the National Lung Screening Trial |
| abstract |
Abstract Based on recent reports from the National Lung Screening Trial (NLST), smokers who were screened by low-dose computer tomography (LDCT) had a 20% lower chance of dying from lung cancer than those screened by chest X-rays. However, due to the complexities of lead time bias and over diagnosis, no formal test has been shown to reduce lung cancer mortality. To correctly evaluate survival benefit due to early detection, it is critical to estimate lead time, the length of time that detection of a disease is advanced by screening. We applied a recently developed probability method to estimate lead time, using the LDCT data from NLST, where human lifetime was treated as random and derived from the actuarial life table of the US Social Security Administration. Using Bayesian posterior samples of key parameters extracted from the NLST-LDCT data, simulations on lead time were carried out on 16 hypothetical cohorts with four initial ages (55, 60, 65, and 70) and four future screening intervals (12, 18, 24, and 30 months). For each scenario, the estimated lead time for both screen-detected and interval cases is reported. Results show that the probability of no-early-detection (interval cases) increases monotonically when the screening interval increases for both genders. A male heavy smoker with an initial screening age at 60 has 11.65% (female 6.76%) chance of no-early-detection with annual screenings. This probability increases to 36.35% (female 28.26%) if the screenings were biennial. The mean lead time appears longer for women than for men. The mean lead time decreases when the screening interval increases, but it appears stable across different initial age groups. These results lay a foundation to evaluate survival benefit accurately with LDCT and to schedule future screening exams. © Springer International Publishing AG, part of Springer Nature 2018 |
| abstractGer |
Abstract Based on recent reports from the National Lung Screening Trial (NLST), smokers who were screened by low-dose computer tomography (LDCT) had a 20% lower chance of dying from lung cancer than those screened by chest X-rays. However, due to the complexities of lead time bias and over diagnosis, no formal test has been shown to reduce lung cancer mortality. To correctly evaluate survival benefit due to early detection, it is critical to estimate lead time, the length of time that detection of a disease is advanced by screening. We applied a recently developed probability method to estimate lead time, using the LDCT data from NLST, where human lifetime was treated as random and derived from the actuarial life table of the US Social Security Administration. Using Bayesian posterior samples of key parameters extracted from the NLST-LDCT data, simulations on lead time were carried out on 16 hypothetical cohorts with four initial ages (55, 60, 65, and 70) and four future screening intervals (12, 18, 24, and 30 months). For each scenario, the estimated lead time for both screen-detected and interval cases is reported. Results show that the probability of no-early-detection (interval cases) increases monotonically when the screening interval increases for both genders. A male heavy smoker with an initial screening age at 60 has 11.65% (female 6.76%) chance of no-early-detection with annual screenings. This probability increases to 36.35% (female 28.26%) if the screenings were biennial. The mean lead time appears longer for women than for men. The mean lead time decreases when the screening interval increases, but it appears stable across different initial age groups. These results lay a foundation to evaluate survival benefit accurately with LDCT and to schedule future screening exams. © Springer International Publishing AG, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Based on recent reports from the National Lung Screening Trial (NLST), smokers who were screened by low-dose computer tomography (LDCT) had a 20% lower chance of dying from lung cancer than those screened by chest X-rays. However, due to the complexities of lead time bias and over diagnosis, no formal test has been shown to reduce lung cancer mortality. To correctly evaluate survival benefit due to early detection, it is critical to estimate lead time, the length of time that detection of a disease is advanced by screening. We applied a recently developed probability method to estimate lead time, using the LDCT data from NLST, where human lifetime was treated as random and derived from the actuarial life table of the US Social Security Administration. Using Bayesian posterior samples of key parameters extracted from the NLST-LDCT data, simulations on lead time were carried out on 16 hypothetical cohorts with four initial ages (55, 60, 65, and 70) and four future screening intervals (12, 18, 24, and 30 months). For each scenario, the estimated lead time for both screen-detected and interval cases is reported. Results show that the probability of no-early-detection (interval cases) increases monotonically when the screening interval increases for both genders. A male heavy smoker with an initial screening age at 60 has 11.65% (female 6.76%) chance of no-early-detection with annual screenings. This probability increases to 36.35% (female 28.26%) if the screenings were biennial. The mean lead time appears longer for women than for men. The mean lead time decreases when the screening interval increases, but it appears stable across different initial age groups. These results lay a foundation to evaluate survival benefit accurately with LDCT and to schedule future screening exams. © Springer International Publishing AG, part of Springer Nature 2018 |
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Estimation of Lead Time via Low-Dose CT in the National Lung Screening Trial |
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Pérez, Adriana Wu, Dongfeng |
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| score |
7.3995953 |