Back to Search Start Over

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

Authors :
Katrine H. Rubin
Peter F. Haastrup
Anne Nicolaisen
Sören Möller
Sonja Wehberg
Sanne Rasmussen
Kirubakaran Balasubramaniam
Jens Søndergaard
Dorte E. Jarbøl
Source :
Cancers; Volume 15; Issue 2; Pages: 487
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Lung cancer can be challenging to diagnose in the early stages, where treatment options are optimal. We aimed to develop 1-year prediction models for the individual risk of incident lung cancer for all individuals aged 40 or above living in Denmark on 1 January 2017. The study was conducted using population-based registers on health and sociodemographics from 2007–2016. We applied backward selection on all variables by logistic regression to develop a risk model for lung cancer and applied the models to the validation cohort, calculated receiver-operating characteristic curves, and estimated the corresponding areas under the curve (AUC). In the populations without and with previously confirmed cancer, 4274/2,826,249 (0.15%) and 482/172,513 (0.3%) individuals received a lung cancer diagnosis in 2017, respectively. For both populations, older age was a relevant predictor, and the most complex models, containing variables related to diagnoses, medication, general practitioner, and specialist contacts, as well as baseline sociodemographic characteristics, had the highest AUC. These models achieved a positive predictive value (PPV) of 0.0127 (0.006) and a negative predictive value (NPV) of 0.989 (0.997) with a 1% cut-off in the population without (with) previous cancer. This corresponds to 1.2% of the screened population experiencing a positive prediction, of which 1.3% would be incident with lung cancer. We have developed and tested a prediction model with a reasonable potential to support clinicians and healthcare planners in identifying patients at risk of lung cancer.

Details

ISSN :
20726694
Volume :
15
Database :
OpenAIRE
Journal :
Cancers
Accession number :
edsair.doi.dedup.....1107dda7e8866a1c5afb8c4c44c7a51b
Full Text :
https://doi.org/10.3390/cancers15020487