Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models
Weiqi Liao, Carol Coupland, Judith Burchardt, David Baldwin, Fergus Gleeson, David Baldwin, George Batchkala, James Buchanan, Judith Burchardt, Rohan Chakraborty, Ravi Chana, Yan Chen, Carol Coupland, Charles Crichton, Jim Davies, Anand Devaraj, Mengran Fan, Julia Hippisley‐Cox, Rositsa Koleva‐Kolarova, Richard Lee, Weiqi Liao, Arjun Nair, L. Pickup, Anne Powell, Jens Rittscher, Amied Shadmaan, Kandavel Shanmugam, Elizabeth A Stokes, Clare Verrill, Johnathan Watkins, Sarah Wordsworth, Fergus Gleeson, Julia Hippisley‐Cox
Abstract
BACKGROUND: Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model-the CanPredict (lung) model-for lung cancer screening in the UK and compare the model performance against seven other risk prediction models. METHODS: , Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55-74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria. FINDINGS: ), as it identified more lung cancer cases than those models by screening the same amount of individuals at high risk. INTERPRETATION: The CanPredict (lung) model was developed, and internally and externally validated, using data from 19·67 million people from two English primary care databases. Our model has potential utility for risk stratification of the UK primary care population and selection of individuals at high risk of lung cancer for targeted screening. If our model is recommended to be implemented in primary care, each individual's risk can be calculated using information in the primary care electronic health records, and people at high risk can be identified for the lung cancer screening programme. FUNDING: Innovate UK (UK Research and Innovation). TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.