A Novel Bayesian Framework For Multi-State Disease Progression Of Lung Cancer

Abstract

CT screening has been commonly used to identify and diagnose lung cancer in its early stages. CT has been shown in clinical studies to reduce lung cancer mortality by 20% as compared to plain chest radiography; however, existing CT screening services face obstacles such as high over diagnosis rates, high costs, and elevated radiation exposure.The study develops computer and deep learning models for predictive lung cancer diagnosis and disease progression prediction in an effort to solve these difficulties. Using a symmetric chain code method and a machine learning system, a novel lung segmentation approach was first developed. The lung nodules connected to the lung wall are included in this process, which minimises over-segmentation error. Finally, to predict the inter disease progression of lung cancer, a Bayesian method was coupled with a prolonged Markov model.The resultant model calculates specific lung cancer state transition data, which can be used to make customised screening recommendations. Extensive trials and results have shown the efficacy of these approaches, paving the way for current CT screening systems to be optimised and improved.