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Developing and Validating a Lung Cancer Risk Prediction Model: A Nationwide Population-Based Study.

Katrine H Rubin^1,2, Peter F Haastrup³, Anne Nicolaisen³

¹OPEN-Open Patient Data Explorative Network, Odense University Hospital, 5000 Odense, Denmark.

|January 21, 2023

Summary

This study developed a 1-year lung cancer risk prediction model for adults 40+, utilizing Danish health registers. The model shows potential for early detection, aiding clinicians in identifying high-risk individuals for lung cancer.

Keywords:

automated risk calculation cancer diagnosis lung cancer prediction models register data socioeconomic status

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Area of Science:

Oncology
Public Health
Biostatistics

Background:

Early diagnosis of lung cancer is crucial for optimal treatment outcomes.
Population-based risk prediction models can aid in identifying individuals at high risk for lung cancer.
Existing diagnostic methods may not effectively identify early-stage lung cancer in the general population.

Purpose of the Study:

To develop and validate 1-year prediction models for incident lung cancer risk.
To identify key predictors for lung cancer development in individuals aged 40 and above.
To assess the performance of these models in a large, population-based cohort.

Main Methods:

Utilized Danish nationwide health and sociodemographic registers from 2007-2016.
Applied logistic regression with backward selection to develop risk prediction models.
Validated models using receiver-operating characteristic curves and calculated area under the curve (AUC).

Main Results:

Older age was a significant predictor for lung cancer.
Complex models incorporating diagnoses, medications, healthcare contacts, and sociodemographics yielded the highest AUC.
Models achieved a negative predictive value (NPV) of 0.989 (without prior cancer) and 0.997 (with prior cancer) at a 1% threshold.

Conclusions:

Developed and validated a 1-year lung cancer risk prediction model with potential clinical utility.
The model can assist healthcare professionals and planners in identifying at-risk populations.
Further refinement could enhance early lung cancer detection and intervention strategies.