Outcome prediction and new treatment paradigms
Work package eight
Clinical and diagnostic data collected throughout our Outcome Prediction and New Treatment Paradigms project will be used to develop better ways to diagnose people earlier and to identify those who would do well with treatment and those who would not.
Clinicians who work in the field of lung cancer know only too well how difficult it can be to diagnose people early when the condition can be cured. Even then, some people will not be cured of lung cancer despite having gone through a significant amount of treatment such as surgery or radiotherapy. We need better ways to diagnose people earlier and to identify the people who do well with treatment and those that do not.
Using clinical and diagnostic data accumulated throughout this project, we are looking for factors that predict the outcome of people with lung cancer. This is being done by using conventional mathematical multivariable analyses as well as machine learning techniques. The aim is not only to identify combinations of variables that predict who has lung cancer but also how successful treatment will be. This will enable more confidence in recommending treatment but importantly, identify who might not do so well. These people with an “unexpected outcome” might be better treated according to novel treatment strategies.
David Baldwin is working with the DART team to develop predictive models and identify those that may be practice-changing.
Researchers who work on this package
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