Demographics and socioeconomic determinants of health predict continued participation in a CT lung cancer screening program.

Autor: Wang Z; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Kim Y; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Mortani Barbosa EJ Jr; Division of Cardiothoracic Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Ground Floor Founders Bldg, Philadelphia, PA 19104, USA. Electronic address: Eduardo.Barbosa@pennmedicine.upenn.edu.
Jazyk: angličtina
Zdroj: Current problems in diagnostic radiology [Curr Probl Diagn Radiol] 2024 Sep-Oct; Vol. 53 (5), pp. 552-559. Date of Electronic Publication: 2024 Apr 21.
DOI: 10.1067/j.cpradiol.2024.04.004
Abstrakt: Purpose: We developed machine learning (ML) models to assess demographic and socioeconomic status (SES) variables' value in predicting continued participation in a low-dose CT lung cancer screening (LCS) program.
Materials and Methods: 480 LCS subjects were retrospectively examined for the following outcomes: (#1) no follow-up (single LCS scan) vs. multiple follow-ups (220 and 260 subjects respectively) and (#2) absent or delayed (>1 month past the due date) follow-up vs timely follow-up (356 and 124 subjects respectively). We quantified the contributions of 14 socioeconomic, demographic, and clinical predictors to LCS adherence, and validated and compared prediction performances of multivariate logistic regression (MLR), support vector machine (SVM) and shallow neural network (NN) models.
Results: For outcome #1, age, sex, race, insurance status, personal cancer history, and median household income were found to be associated with returning for follow-ups. For outcome #2, age, sex, race, and insurance status were significant predictor of absent/delayed LCS follow-up. Across 5-fold cross-validation, the MLR model achieved an average AUC of 0.732 (95% CI, 0.661-0.803) for outcome #1 and 0.633 (95% CI, 0.602-0.664) for outcome #2 and is the model with best predictive performance overall, whereas NN and SVM tended to overfit training data and fell short on testing data performance for either outcome.
Conclusions: We identified significant predictors of LCS adherence, and our ML models can predict which subjects are at higher risk of receiving no or delayed LCS follow-ups. Our results could inform data-driven interventions to engage vulnerable populations and extend the benefits of LCS.
Competing Interests: Declaration of competing interest Dr. Mortani Barbosa Jr. has received research grants from Siemens Healthnieers, unrelated to this study
(Copyright © 2024. Published by Elsevier Inc.)
Databáze: MEDLINE
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