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Scan-based competing death risk model for re-evaluating lung cancer computed tomography screening eligibility.

About this Publication
Title
Scan-based competing death risk model for re-evaluating lung cancer computed tomography screening eligibility.
Pubmed ID
34649976 (View this publication on the PubMed website)
Digital Object Identifier
Publication
Eur Respir J. 2021 Oct 14
Authors
Schreuder A, Jacobs C, Lessmann N, Broeders MJM, Silva M, Išgum I, de Jong PA, van den Heuvel MM, Sverzellati N, Prokop M, Pastorino U, Schaefer-Prokop CM, van Ginneken B
Affiliations
  • Dept of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands antoniusschreuder@gmail.com.
  • Dept of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Unit of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
  • Dept of Biomedical Engineering and Physics, Amsterdam UMC - Location AMC, Amsterdam, The Netherlands.
  • Dept of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands.
  • Dept of Respiratory Diseases, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Section of Radiology, Unit of Surgical Sciences, Dept of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
Abstract

PURPOSE: A baseline CT scan for lung cancer (LC) screening may reveal information indicating that certain LC screening participants can be screened less, and instead require dedicated early cardiac and respiratory clinical input. We aimed to develop and validate competing death (CD) risk models using CT information to identify participants with a low LC and a high CD risk.

METHODS: Participant demographics and quantitative CT measures of LC, cardiovascular disease, and chronic obstructive pulmonary disease were considered for deriving a logistic regression model for predicting five-year CD risk using a sample from the National Lung Screening Trial (n=15 000). Multicentric Italian Lung Detection data was used to perform external validation (n=2287).

RESULTS: Our final CD model outperformed an external pre-scan model (CDRAT) in both the derivation (Area under the curve=0.744 [95% confidence interval=0.727 to 0.761] and 0.677 [0.658 to 0.695], respectively) and validation cohorts (0.744 [0.652 to 0.835] and 0.725 [0.633 to 0.816], respectively). By also taking LC incidence risk into consideration, we suggested a risk threshold where a subgroup (6258/23 096, 27%) was identified with a number needed to screen to detect one LC of 216 (versus 23 in the remainder of the cohort) and ratio of 5.41 CDs per LC case (versus 0.88). The respective values in the validation cohort subgroup (774/2287, 34%) were 129 (versus 29) and 1.67 (versus 0.43).

CONCLUSIONS: Evaluating both LC and CD risks post-scan may improve the efficiency of LC screening and facilitate the initiation of multidisciplinary trajectories among certain participants.

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