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Evaluating the clinical trends and benefits of low‐dose computed tomography in lung cancer patients

BACKGROUND: Despite guideline recommendations, utilization of low‐dose computed tomography (LDCT) for lung cancer screening remains low. The driving factors behind these low rates and the real‐world effect of LDCT utilization on lung cancer outcomes remain limited. METHODS: We identified patients di...

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Autores principales: Qiao, Edmund M., Voora, Rohith S., Nalawade, Vinit, Kotha, Nikhil V., Qian, Alexander S., Nelson, Tyler J., Durkin, Michael, Vitzthum, Lucas K., Murphy, James D., Stewart, Tyler F., Rose, Brent S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8525167/
https://www.ncbi.nlm.nih.gov/pubmed/34528761
http://dx.doi.org/10.1002/cam4.4229
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author Qiao, Edmund M.
Voora, Rohith S.
Nalawade, Vinit
Kotha, Nikhil V.
Qian, Alexander S.
Nelson, Tyler J.
Durkin, Michael
Vitzthum, Lucas K.
Murphy, James D.
Stewart, Tyler F.
Rose, Brent S.
author_facet Qiao, Edmund M.
Voora, Rohith S.
Nalawade, Vinit
Kotha, Nikhil V.
Qian, Alexander S.
Nelson, Tyler J.
Durkin, Michael
Vitzthum, Lucas K.
Murphy, James D.
Stewart, Tyler F.
Rose, Brent S.
author_sort Qiao, Edmund M.
collection PubMed
description BACKGROUND: Despite guideline recommendations, utilization of low‐dose computed tomography (LDCT) for lung cancer screening remains low. The driving factors behind these low rates and the real‐world effect of LDCT utilization on lung cancer outcomes remain limited. METHODS: We identified patients diagnosed with non‐small cell lung cancer (NSCLC) from 2015 to 2017 within the Veterans Health Administration. Multivariable logistic regression assessed the influence of LDCT screening on stage at diagnosis. Lead time correction using published LDCT lead times was performed. Cancer‐specific mortality (CSM) was evaluated using Fine–Gray regression with non‐cancer death as a competing risk. A lasso machine learning model identified important predictors for receiving LDCT screening. RESULTS: Among 4664 patients, mean age was 67.8 with 58‐month median follow‐up, 95% CI = [7–71], and 118 patients received ≥1 screening LDCT before NSCLC diagnosis. From 2015 to 2017, LDCT screening increased (0.1%–6.6%, mean = 1.3%). Compared with no screening, patients with ≥1 LDCT were more than twice as likely to present with stage I disease at diagnosis (odds ratio [OR] 2.16 [95% CI 1.46–3.20]) and less than half as likely to present with stage IV (OR 0.38 [CI 0.21–0.70]). Screened patients had lower risk of CSM even after adjusting for LDCT lead time (subdistribution hazard ratio 0.60 [CI 0.42–0.85]). The machine learning model achieved an area under curve of 0.87 and identified diagnosis year and region as the most important predictors for receiving LDCT. White, non‐Hispanic patients were more likely to receive LDCT screening, whereas minority, older, female, and unemployed patients were less likely. CONCLUSIONS: Utilization of LDCT screening is increasing, although remains low. Consistent with randomized data, LDCT‐screened patients were diagnosed at earlier stages and had lower CSM. LDCT availability appeared to be the main predictor of utilization. Providing access to more patients, including those in diverse racial and socioeconomic groups, should be a priority.
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spelling pubmed-85251672021-10-26 Evaluating the clinical trends and benefits of low‐dose computed tomography in lung cancer patients Qiao, Edmund M. Voora, Rohith S. Nalawade, Vinit Kotha, Nikhil V. Qian, Alexander S. Nelson, Tyler J. Durkin, Michael Vitzthum, Lucas K. Murphy, James D. Stewart, Tyler F. Rose, Brent S. Cancer Med Cancer Prevention BACKGROUND: Despite guideline recommendations, utilization of low‐dose computed tomography (LDCT) for lung cancer screening remains low. The driving factors behind these low rates and the real‐world effect of LDCT utilization on lung cancer outcomes remain limited. METHODS: We identified patients diagnosed with non‐small cell lung cancer (NSCLC) from 2015 to 2017 within the Veterans Health Administration. Multivariable logistic regression assessed the influence of LDCT screening on stage at diagnosis. Lead time correction using published LDCT lead times was performed. Cancer‐specific mortality (CSM) was evaluated using Fine–Gray regression with non‐cancer death as a competing risk. A lasso machine learning model identified important predictors for receiving LDCT screening. RESULTS: Among 4664 patients, mean age was 67.8 with 58‐month median follow‐up, 95% CI = [7–71], and 118 patients received ≥1 screening LDCT before NSCLC diagnosis. From 2015 to 2017, LDCT screening increased (0.1%–6.6%, mean = 1.3%). Compared with no screening, patients with ≥1 LDCT were more than twice as likely to present with stage I disease at diagnosis (odds ratio [OR] 2.16 [95% CI 1.46–3.20]) and less than half as likely to present with stage IV (OR 0.38 [CI 0.21–0.70]). Screened patients had lower risk of CSM even after adjusting for LDCT lead time (subdistribution hazard ratio 0.60 [CI 0.42–0.85]). The machine learning model achieved an area under curve of 0.87 and identified diagnosis year and region as the most important predictors for receiving LDCT. White, non‐Hispanic patients were more likely to receive LDCT screening, whereas minority, older, female, and unemployed patients were less likely. CONCLUSIONS: Utilization of LDCT screening is increasing, although remains low. Consistent with randomized data, LDCT‐screened patients were diagnosed at earlier stages and had lower CSM. LDCT availability appeared to be the main predictor of utilization. Providing access to more patients, including those in diverse racial and socioeconomic groups, should be a priority. John Wiley and Sons Inc. 2021-09-16 /pmc/articles/PMC8525167/ /pubmed/34528761 http://dx.doi.org/10.1002/cam4.4229 Text en © 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cancer Prevention
Qiao, Edmund M.
Voora, Rohith S.
Nalawade, Vinit
Kotha, Nikhil V.
Qian, Alexander S.
Nelson, Tyler J.
Durkin, Michael
Vitzthum, Lucas K.
Murphy, James D.
Stewart, Tyler F.
Rose, Brent S.
Evaluating the clinical trends and benefits of low‐dose computed tomography in lung cancer patients
title Evaluating the clinical trends and benefits of low‐dose computed tomography in lung cancer patients
title_full Evaluating the clinical trends and benefits of low‐dose computed tomography in lung cancer patients
title_fullStr Evaluating the clinical trends and benefits of low‐dose computed tomography in lung cancer patients
title_full_unstemmed Evaluating the clinical trends and benefits of low‐dose computed tomography in lung cancer patients
title_short Evaluating the clinical trends and benefits of low‐dose computed tomography in lung cancer patients
title_sort evaluating the clinical trends and benefits of low‐dose computed tomography in lung cancer patients
topic Cancer Prevention
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8525167/
https://www.ncbi.nlm.nih.gov/pubmed/34528761
http://dx.doi.org/10.1002/cam4.4229
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