Risk models
lung cancer, malignancy, NELSON
LUNG CANCER, REGRESSION
To address the 5th and 6th objectives, multi-source prediction models will be developed for (1) selecting participants at the highest risk of lung cancer for CT screening, and (2) optimized lung nodule discrimination. Both models will be developed for baseline and incident screening rounds using combined data from NELSON and DLCST studies where possible. For developing the model for participant selection at baseline, the data from self-report questionnaires, genetic and environmental risk scores will be used. For the incident rounds, the participant selection model will also include AI-risk score for lung nodule malignancy and CT imaging biomarkers beyond lung nodules. For developing the model for lung nodule discrimination at baseline, the aforementioned data for the participant selection model will be combined with AI-risk score for lung nodule malignancy and CT imaging biomarkers beyond lung nodules, as well as lung function results (available in a NELSON subset).Machine-learning methods will be applied to (re)classify lung cancer risk and nodule discrimination after each round, using generated and existing participant/nodule information. Logistic regression will be compared with off-the-shelf machine-learning methods such as support vector machines and gradient boosted trees. To rank the classification models, we will use the average PR AUC from the outer cross-validation loop and compare the 