Lung Cancer Detection Using Bayesian Networks: A Retrospective Development and Validation Study on a Danish Population of High-Risk Individuals

Margrethe Bang Henriksen*, Florian Van Daalen, Leonard Wee, Torben Frostrup Hansen, Lars Henrik Jensen, Claus Lohman Brasen, Ole Hilberg, Inigo Bermejo

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

BackgroundLung cancer (LC) is the top cause of cancer deaths globally, prompting many countries to adopt LC screening programs. While screening typically relies on age and smoking intensity, more efficient risk models exist. We devised a Bayesian network (BN) for LC detection, testing its resilience with varying degrees of missing data and comparing it to a prior machine learning (ML) model.MethodsWe analyzed data from 9940 patients referred for LC assessment in Southern Denmark from 2009 to 2018. Variables included age, sex, smoking, and lab results. Our experiments varied missing data (0%-30%), BN structure (expert-based vs. data-driven), and discretization method (standard vs. data-driven).ResultsAcross all missing data levels, area under the curve (AUC) remained steady, ranging from 0.737 to 0.757, compared to the ML model's AUC of 0.77. BN structure and discretization method had minimal impact on performance. BNs were well calibrated overall, with a net benefit in decision curve analysis when predicted risk exceeded 5%.ConclusionBN models showed resilience with up to 30% missing values. Moreover, these BNs exhibited similar performance, calibration, and clinical utility compared to the machine learning model developed using the same dataset. Considering their effectiveness in handling missing data, BNs emerge as a relevant method for the development of future lung cancer detection models.
Original languageEnglish
Article numbere70458
Number of pages14
JournalCancer Medicine
Volume14
Issue number3
DOIs
Publication statusPublished - 1 Feb 2025

Keywords

  • SCREENING TRIAL
  • PREDICTION
  • CRITERIA
  • MODELS

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