Improving the PLCOm2012 Lung Cancer Risk Prediction Model by Incorporating Air Pollution, Occupational/Environmental, Dietary/Nutritional, and Genetic Data
Approved Research ID: 84086
Approval date: January 31st 2023
Worldwide, lung cancer is the leading cause of cancer death. Lung cancer is usually diagnosed at an advanced stage, which has a poor prognosis. Recently, research studies have convincingly shown that lung cancer screening of high-risk people with low dose computed tomography scans can detect lung cancers early, when they can be cured. Lung cancer screening can reduce lung cancer deaths by 20% or more. The key to successful screening is identifying high-risk individuals for screening. The PLCOm2012 is a mathematical model that accurately predicts who will develop lung cancer. Although the PLCOm2012 model predicts well, it can be improved. Our study will attempt to enhance the PLCOm2012's ability to predict by adding to it additional risk factors for lung cancer. These include air pollution, occupation, dietary and nutritional factors, and genetic factors, which to date have not been included in lung cancer prediction models. Currently, the PLCOm2012 is being used or is being planned for use for selecting individuals for lung cancer screening in multiple countries around the world. By making the PLCOm2012 model more accurate, more people who will develop lung cancer can be selected for screening and fewer individuals who will not develop lung cancer will be subjected to screening and possible harms related to screening. Many more lung cancer deaths can be avoided. Also, the new information we learn about air pollution, occupation, and diet/nutrition, may make possible public health interventions and recommendations that might prevent lung cancers from occurring.
In many locations around the world, lung cancer rates appear to be increasing in individuals who have never smoked. Our research may produce a model that can predict lung cancer in never-smokers, which may make effective lung cancer screening possible in them.
Because our study entails, many sophisticated, complex analyses, we expect it to take three years or more. However, we anticipate being able to publish some important practical findings within one year.