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Title: Validating and updating lung cancer prediction models
Author: Gray, Eoin
ISNI:       0000 0004 6494 1344
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2018
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Lung cancer is a global disease that affect millions of individuals worldwide. Additionally, the disease is beset with a poor 5-year survival rate, a direct consequence of a low early stage diagnosis rate. In an attempt to improve lung cancer prognosis, individuals at high risk of developing lung cancer should be identified for periodic screening. Prediction models are devised to predict an individual’s risk of developing a disease over a specified time period. These can be used to identify high risk individuals and be made publically available to allow individuals’ to be conscience of their own risk. While prediction models have multiple uses it is imperative the models demonstrate a good standard of performance consistently when reviewed. The project conducted a systematic review, analysing previously published lung cancer prediction models. The review identified that there had been inadequate reporting of the existing models and when these models have been validated this had not been consistent across different publications. As a consequence models have not been consistently considered as a selective screening tool. The project then validated the prediction models using datasets from the International Lung Cancer Consortium. The validation identified the leading models which will allow a more targeted focus on these models in future research. This could culminate in the model being implemented as a clinical utility. The final stage reviewed methods to update a single prediction model or aggregate multiple prediction models into a meta-model. A literature review identified and evaluated the different methods, discussing how different methods can be successful in different scenarios. The methods were also reviewed for their suitability updating selected lung cancer prediction models, and appropriate methods were identified. These were then applied to create updated lung cancer models which were validated to assess which methods were successful at improving the performance and robustness of lung cancer prediction models. As lung cancer research develops, particularly into researching genetic markers that may explain lung cancer risk, these factors could be incorporated into already successful prediction models using appropriate model updating methods that were identified in our research.
Supervisor: Teare, M. Dawn ; Stevens, John Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available