Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method

Summary: Background: Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods: A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings: The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0·968 (SD 0·013), 0·946 (0·013), and 0·899 (0·017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16·07, 95% CI 10·15–25·44; p<0·0001) among people with high scores on Lung-RADS. Interpretation: DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan. Funding: Allegheny Health Network, Johns Hopkins University, Terry Fox Research Institute, and British Columbia Cancer Foundation. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Peng Huang, PhD [verfasserIn] Cheng T Lin, MD [verfasserIn] Yuliang Li, MS [verfasserIn] Martin C Tammemagi, ProfPhD [verfasserIn] Malcolm V Brock, ProfMD [verfasserIn] Sukhinder Atkar-Khattra, BSc [verfasserIn] Yanxun Xu, PhD [verfasserIn] Ping Hu, ScD [verfasserIn] John R Mayo, ProfMD [verfasserIn] Heidi Schmidt, ProfMD [verfasserIn] Michel Gingras, MD [verfasserIn] Sergio Pasian, MD [verfasserIn] Lori Stewart, MD [verfasserIn] Scott Tsai, MD [verfasserIn] Jean M Seely, MD [verfasserIn] Daria Manos, MD [verfasserIn] Paul Burrowes, MD [verfasserIn] Rick Bhatia, MD [verfasserIn] Ming-Sound Tsao, ProfMD [verfasserIn] Stephen Lam, ProfMD [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Übergeordnetes Werk:	In: The Lancet: Digital Health - Elsevier, 2019, 1(2019), 7, Seite e353-e362
Übergeordnetes Werk:	volume:1 ; year:2019 ; number:7 ; pages:e353-e362

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/S2589-7500(19)30159-1

Katalog-ID:	DOAJ047746750

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520			\|a Summary: Background: Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods: A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings: The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0·968 (SD 0·013), 0·946 (0·013), and 0·899 (0·017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16·07, 95% CI 10·15–25·44; p<0·0001) among people with high scores on Lung-RADS. Interpretation: DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan. Funding: Allegheny Health Network, Johns Hopkins University, Terry Fox Research Institute, and British Columbia Cancer Foundation.
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700	0		\|a Cheng T Lin, MD \|e verfasserin \|4 aut
700	0		\|a Yuliang Li, MS \|e verfasserin \|4 aut
700	0		\|a Martin C Tammemagi, ProfPhD \|e verfasserin \|4 aut
700	0		\|a Malcolm V Brock, ProfMD \|e verfasserin \|4 aut
700	0		\|a Sukhinder Atkar-Khattra, BSc \|e verfasserin \|4 aut
700	0		\|a Yanxun Xu, PhD \|e verfasserin \|4 aut
700	0		\|a Ping Hu, ScD \|e verfasserin \|4 aut
700	0		\|a John R Mayo, ProfMD \|e verfasserin \|4 aut
700	0		\|a Heidi Schmidt, ProfMD \|e verfasserin \|4 aut
700	0		\|a Michel Gingras, MD \|e verfasserin \|4 aut
700	0		\|a Sergio Pasian, MD \|e verfasserin \|4 aut
700	0		\|a Lori Stewart, MD \|e verfasserin \|4 aut
700	0		\|a Scott Tsai, MD \|e verfasserin \|4 aut
700	0		\|a Jean M Seely, MD \|e verfasserin \|4 aut
700	0		\|a Daria Manos, MD \|e verfasserin \|4 aut
700	0		\|a Paul Burrowes, MD \|e verfasserin \|4 aut
700	0		\|a Rick Bhatia, MD \|e verfasserin \|4 aut
700	0		\|a Ming-Sound Tsao, ProfMD \|e verfasserin \|4 aut
700	0		\|a Stephen Lam, ProfMD \|e verfasserin \|4 aut
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allfields	10.1016/S2589-7500(19)30159-1 doi (DE-627)DOAJ047746750 (DE-599)DOAJ96d0a4b41cf44df180c556597a54738b DE-627 ger DE-627 rakwb eng R858-859.7 Peng Huang, PhD verfasserin aut Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: Background: Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods: A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings: The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0·968 (SD 0·013), 0·946 (0·013), and 0·899 (0·017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16·07, 95% CI 10·15–25·44; p<0·0001) among people with high scores on Lung-RADS. Interpretation: DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan. Funding: Allegheny Health Network, Johns Hopkins University, Terry Fox Research Institute, and British Columbia Cancer Foundation. Computer applications to medicine. Medical informatics Cheng T Lin, MD verfasserin aut Yuliang Li, MS verfasserin aut Martin C Tammemagi, ProfPhD verfasserin aut Malcolm V Brock, ProfMD verfasserin aut Sukhinder Atkar-Khattra, BSc verfasserin aut Yanxun Xu, PhD verfasserin aut Ping Hu, ScD verfasserin aut John R Mayo, ProfMD verfasserin aut Heidi Schmidt, ProfMD verfasserin aut Michel Gingras, MD verfasserin aut Sergio Pasian, MD verfasserin aut Lori Stewart, MD verfasserin aut Scott Tsai, MD verfasserin aut Jean M Seely, MD verfasserin aut Daria Manos, MD verfasserin aut Paul Burrowes, MD verfasserin aut Rick Bhatia, MD verfasserin aut Ming-Sound Tsao, ProfMD verfasserin aut Stephen Lam, ProfMD verfasserin aut In The Lancet: Digital Health Elsevier, 2019 1(2019), 7, Seite e353-e362 (DE-627)1665782404 25897500 nnns volume:1 year:2019 number:7 pages:e353-e362 https://doi.org/10.1016/S2589-7500(19)30159-1 kostenfrei https://doaj.org/article/96d0a4b41cf44df180c556597a54738b kostenfrei http://www.sciencedirect.com/science/article/pii/S2589750019301591 kostenfrei https://doaj.org/toc/2589-7500 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 7 e353-e362
spelling	10.1016/S2589-7500(19)30159-1 doi (DE-627)DOAJ047746750 (DE-599)DOAJ96d0a4b41cf44df180c556597a54738b DE-627 ger DE-627 rakwb eng R858-859.7 Peng Huang, PhD verfasserin aut Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: Background: Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods: A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings: The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0·968 (SD 0·013), 0·946 (0·013), and 0·899 (0·017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16·07, 95% CI 10·15–25·44; p<0·0001) among people with high scores on Lung-RADS. Interpretation: DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan. Funding: Allegheny Health Network, Johns Hopkins University, Terry Fox Research Institute, and British Columbia Cancer Foundation. Computer applications to medicine. Medical informatics Cheng T Lin, MD verfasserin aut Yuliang Li, MS verfasserin aut Martin C Tammemagi, ProfPhD verfasserin aut Malcolm V Brock, ProfMD verfasserin aut Sukhinder Atkar-Khattra, BSc verfasserin aut Yanxun Xu, PhD verfasserin aut Ping Hu, ScD verfasserin aut John R Mayo, ProfMD verfasserin aut Heidi Schmidt, ProfMD verfasserin aut Michel Gingras, MD verfasserin aut Sergio Pasian, MD verfasserin aut Lori Stewart, MD verfasserin aut Scott Tsai, MD verfasserin aut Jean M Seely, MD verfasserin aut Daria Manos, MD verfasserin aut Paul Burrowes, MD verfasserin aut Rick Bhatia, MD verfasserin aut Ming-Sound Tsao, ProfMD verfasserin aut Stephen Lam, ProfMD verfasserin aut In The Lancet: Digital Health Elsevier, 2019 1(2019), 7, Seite e353-e362 (DE-627)1665782404 25897500 nnns volume:1 year:2019 number:7 pages:e353-e362 https://doi.org/10.1016/S2589-7500(19)30159-1 kostenfrei https://doaj.org/article/96d0a4b41cf44df180c556597a54738b kostenfrei http://www.sciencedirect.com/science/article/pii/S2589750019301591 kostenfrei https://doaj.org/toc/2589-7500 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 7 e353-e362
allfields_unstemmed	10.1016/S2589-7500(19)30159-1 doi (DE-627)DOAJ047746750 (DE-599)DOAJ96d0a4b41cf44df180c556597a54738b DE-627 ger DE-627 rakwb eng R858-859.7 Peng Huang, PhD verfasserin aut Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: Background: Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods: A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings: The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0·968 (SD 0·013), 0·946 (0·013), and 0·899 (0·017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16·07, 95% CI 10·15–25·44; p<0·0001) among people with high scores on Lung-RADS. Interpretation: DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan. Funding: Allegheny Health Network, Johns Hopkins University, Terry Fox Research Institute, and British Columbia Cancer Foundation. Computer applications to medicine. Medical informatics Cheng T Lin, MD verfasserin aut Yuliang Li, MS verfasserin aut Martin C Tammemagi, ProfPhD verfasserin aut Malcolm V Brock, ProfMD verfasserin aut Sukhinder Atkar-Khattra, BSc verfasserin aut Yanxun Xu, PhD verfasserin aut Ping Hu, ScD verfasserin aut John R Mayo, ProfMD verfasserin aut Heidi Schmidt, ProfMD verfasserin aut Michel Gingras, MD verfasserin aut Sergio Pasian, MD verfasserin aut Lori Stewart, MD verfasserin aut Scott Tsai, MD verfasserin aut Jean M Seely, MD verfasserin aut Daria Manos, MD verfasserin aut Paul Burrowes, MD verfasserin aut Rick Bhatia, MD verfasserin aut Ming-Sound Tsao, ProfMD verfasserin aut Stephen Lam, ProfMD verfasserin aut In The Lancet: Digital Health Elsevier, 2019 1(2019), 7, Seite e353-e362 (DE-627)1665782404 25897500 nnns volume:1 year:2019 number:7 pages:e353-e362 https://doi.org/10.1016/S2589-7500(19)30159-1 kostenfrei https://doaj.org/article/96d0a4b41cf44df180c556597a54738b kostenfrei http://www.sciencedirect.com/science/article/pii/S2589750019301591 kostenfrei https://doaj.org/toc/2589-7500 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 7 e353-e362
allfieldsGer	10.1016/S2589-7500(19)30159-1 doi (DE-627)DOAJ047746750 (DE-599)DOAJ96d0a4b41cf44df180c556597a54738b DE-627 ger DE-627 rakwb eng R858-859.7 Peng Huang, PhD verfasserin aut Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: Background: Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods: A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings: The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0·968 (SD 0·013), 0·946 (0·013), and 0·899 (0·017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16·07, 95% CI 10·15–25·44; p<0·0001) among people with high scores on Lung-RADS. Interpretation: DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan. Funding: Allegheny Health Network, Johns Hopkins University, Terry Fox Research Institute, and British Columbia Cancer Foundation. Computer applications to medicine. Medical informatics Cheng T Lin, MD verfasserin aut Yuliang Li, MS verfasserin aut Martin C Tammemagi, ProfPhD verfasserin aut Malcolm V Brock, ProfMD verfasserin aut Sukhinder Atkar-Khattra, BSc verfasserin aut Yanxun Xu, PhD verfasserin aut Ping Hu, ScD verfasserin aut John R Mayo, ProfMD verfasserin aut Heidi Schmidt, ProfMD verfasserin aut Michel Gingras, MD verfasserin aut Sergio Pasian, MD verfasserin aut Lori Stewart, MD verfasserin aut Scott Tsai, MD verfasserin aut Jean M Seely, MD verfasserin aut Daria Manos, MD verfasserin aut Paul Burrowes, MD verfasserin aut Rick Bhatia, MD verfasserin aut Ming-Sound Tsao, ProfMD verfasserin aut Stephen Lam, ProfMD verfasserin aut In The Lancet: Digital Health Elsevier, 2019 1(2019), 7, Seite e353-e362 (DE-627)1665782404 25897500 nnns volume:1 year:2019 number:7 pages:e353-e362 https://doi.org/10.1016/S2589-7500(19)30159-1 kostenfrei https://doaj.org/article/96d0a4b41cf44df180c556597a54738b kostenfrei http://www.sciencedirect.com/science/article/pii/S2589750019301591 kostenfrei https://doaj.org/toc/2589-7500 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 7 e353-e362
allfieldsSound	10.1016/S2589-7500(19)30159-1 doi (DE-627)DOAJ047746750 (DE-599)DOAJ96d0a4b41cf44df180c556597a54738b DE-627 ger DE-627 rakwb eng R858-859.7 Peng Huang, PhD verfasserin aut Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: Background: Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods: A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings: The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0·968 (SD 0·013), 0·946 (0·013), and 0·899 (0·017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16·07, 95% CI 10·15–25·44; p<0·0001) among people with high scores on Lung-RADS. Interpretation: DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan. Funding: Allegheny Health Network, Johns Hopkins University, Terry Fox Research Institute, and British Columbia Cancer Foundation. Computer applications to medicine. Medical informatics Cheng T Lin, MD verfasserin aut Yuliang Li, MS verfasserin aut Martin C Tammemagi, ProfPhD verfasserin aut Malcolm V Brock, ProfMD verfasserin aut Sukhinder Atkar-Khattra, BSc verfasserin aut Yanxun Xu, PhD verfasserin aut Ping Hu, ScD verfasserin aut John R Mayo, ProfMD verfasserin aut Heidi Schmidt, ProfMD verfasserin aut Michel Gingras, MD verfasserin aut Sergio Pasian, MD verfasserin aut Lori Stewart, MD verfasserin aut Scott Tsai, MD verfasserin aut Jean M Seely, MD verfasserin aut Daria Manos, MD verfasserin aut Paul Burrowes, MD verfasserin aut Rick Bhatia, MD verfasserin aut Ming-Sound Tsao, ProfMD verfasserin aut Stephen Lam, ProfMD verfasserin aut In The Lancet: Digital Health Elsevier, 2019 1(2019), 7, Seite e353-e362 (DE-627)1665782404 25897500 nnns volume:1 year:2019 number:7 pages:e353-e362 https://doi.org/10.1016/S2589-7500(19)30159-1 kostenfrei https://doaj.org/article/96d0a4b41cf44df180c556597a54738b kostenfrei http://www.sciencedirect.com/science/article/pii/S2589750019301591 kostenfrei https://doaj.org/toc/2589-7500 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 7 e353-e362
language	English
source	In The Lancet: Digital Health 1(2019), 7, Seite e353-e362 volume:1 year:2019 number:7 pages:e353-e362
sourceStr	In The Lancet: Digital Health 1(2019), 7, Seite e353-e362 volume:1 year:2019 number:7 pages:e353-e362
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Computer applications to medicine. Medical informatics
isfreeaccess_bool	true
container_title	The Lancet: Digital Health
authorswithroles_txt_mv	Peng Huang, PhD @@aut@@ Cheng T Lin, MD @@aut@@ Yuliang Li, MS @@aut@@ Martin C Tammemagi, ProfPhD @@aut@@ Malcolm V Brock, ProfMD @@aut@@ Sukhinder Atkar-Khattra, BSc @@aut@@ Yanxun Xu, PhD @@aut@@ Ping Hu, ScD @@aut@@ John R Mayo, ProfMD @@aut@@ Heidi Schmidt, ProfMD @@aut@@ Michel Gingras, MD @@aut@@ Sergio Pasian, MD @@aut@@ Lori Stewart, MD @@aut@@ Scott Tsai, MD @@aut@@ Jean M Seely, MD @@aut@@ Daria Manos, MD @@aut@@ Paul Burrowes, MD @@aut@@ Rick Bhatia, MD @@aut@@ Ming-Sound Tsao, ProfMD @@aut@@ Stephen Lam, ProfMD @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	1665782404
id	DOAJ047746750
language_de	englisch
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We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods: A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings: The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0·968 (SD 0·013), 0·946 (0·013), and 0·899 (0·017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16·07, 95% CI 10·15–25·44; p<0·0001) among people with high scores on Lung-RADS. Interpretation: DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. 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callnumber-first	R - Medicine
author	Peng Huang, PhD
spellingShingle	Peng Huang, PhD misc R858-859.7 misc Computer applications to medicine. Medical informatics Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method
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topic_title	R858-859.7 Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method
topic	misc R858-859.7 misc Computer applications to medicine. Medical informatics
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title	Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method
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title_full	Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method
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author_browse	Peng Huang, PhD Cheng T Lin, MD Yuliang Li, MS Martin C Tammemagi, ProfPhD Malcolm V Brock, ProfMD Sukhinder Atkar-Khattra, BSc Yanxun Xu, PhD Ping Hu, ScD John R Mayo, ProfMD Heidi Schmidt, ProfMD Michel Gingras, MD Sergio Pasian, MD Lori Stewart, MD Scott Tsai, MD Jean M Seely, MD Daria Manos, MD Paul Burrowes, MD Rick Bhatia, MD Ming-Sound Tsao, ProfMD Stephen Lam, ProfMD
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title_sort	prediction of lung cancer risk at follow-up screening with low-dose ct: a training and validation study of a deep learning method
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title_auth	Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method
abstract	Summary: Background: Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods: A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings: The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0·968 (SD 0·013), 0·946 (0·013), and 0·899 (0·017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16·07, 95% CI 10·15–25·44; p<0·0001) among people with high scores on Lung-RADS. Interpretation: DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan. Funding: Allegheny Health Network, Johns Hopkins University, Terry Fox Research Institute, and British Columbia Cancer Foundation.
abstractGer	Summary: Background: Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods: A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings: The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0·968 (SD 0·013), 0·946 (0·013), and 0·899 (0·017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16·07, 95% CI 10·15–25·44; p<0·0001) among people with high scores on Lung-RADS. Interpretation: DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan. Funding: Allegheny Health Network, Johns Hopkins University, Terry Fox Research Institute, and British Columbia Cancer Foundation.
abstract_unstemmed	Summary: Background: Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods: A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings: The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0·968 (SD 0·013), 0·946 (0·013), and 0·899 (0·017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16·07, 95% CI 10·15–25·44; p<0·0001) among people with high scores on Lung-RADS. Interpretation: DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan. Funding: Allegheny Health Network, Johns Hopkins University, Terry Fox Research Institute, and British Columbia Cancer Foundation.
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title_short	Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method
url	https://doi.org/10.1016/S2589-7500(19)30159-1 https://doaj.org/article/96d0a4b41cf44df180c556597a54738b http://www.sciencedirect.com/science/article/pii/S2589750019301591 https://doaj.org/toc/2589-7500
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up_date	2024-07-03T13:53:50.626Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ047746750</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502233930.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/S2589-7500(19)30159-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ047746750</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ96d0a4b41cf44df180c556597a54738b</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="050" ind1=" " ind2="0"><subfield code="a">R858-859.7</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Peng Huang, PhD</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</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="520" ind1=" " ind2=" "><subfield code="a">Summary: Background: Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods: A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings: The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0·968 (SD 0·013), 0·946 (0·013), and 0·899 (0·017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16·07, 95% CI 10·15–25·44; p<0·0001) among people with high scores on Lung-RADS. Interpretation: DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan. Funding: Allegheny Health Network, Johns Hopkins University, Terry Fox Research Institute, and British Columbia Cancer Foundation.</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computer applications to medicine. 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Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method

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