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.FootnotesThis manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article.Conflict of interest: Dr. Schreuder has nothing to disclose.Conflict of interest: Dr. Jacobs reports grants from MeVis Medical Solutions AG, Bremen, Germany, outside the submitted work; .Conflict of interest: Dr. Lessmann has nothing to disclose.Conflict of interest: Dr. Broeders has nothing to disclose.Conflict of interest: Dr. Silva has nothing to disclose.Conflict of interest: Dr. I\v sgum has nothing to disclose.Conflict of interest: Dr. de Jong reports other from Philips Healthcare, during the conduct of the study; .Conflict of interest: Dr. van den Heuvel has nothing to disclose.Conflict of interest: Dr. Sverzellati has nothing to disclose.Conflict of interest: Dr. Prokop reports personal fees from Bracco, Bayer, Toshiba, \& Siemens, grants from Toshiba, other from Thiroux, outside the submitted work; .Conflict of interest: Dr. Pastorino has nothing to disclose.Conflict of interest: Dr. Schaefer-Prokop has nothing to disclose.Conflict of interest: Dr. van Ginneken reports other from Thirona, grants from Mevis Medical Solutions, grants from Delft Imaging Systems, outside the submitted work; .
Scan-based competing death risk model for reevaluating lung cancer computed tomography screening eligibility
A. Schreuder, C. Jacobs, N. Lessmann, M. Broeders, M. Silva, I. Išgum, P. de Jong, M. van den Heuvel, N. Sverzellati, M. Prokop, U. Pastorino, C. Schaefer-Prokop and B. van Ginneken
European Respiratory Journal 2022;59(5):2101613.