Lung-ORACLE

• Research type

  Research Study

• Full title

  Investigating the utility of machine learning methods to predict prognosis and guide treatment decisions for people with lung cancer

• IRAS ID

  307147

• Contact name

  Neal Navani

• Contact email

  n.navani@ucl.ac.uk

• Sponsor organisation

  University College London

• Clinicaltrials.gov Identifier

  146449, Edge ID; Z6364106/202205/53, UCL Data Protection Number

• Duration of Study in the UK

  2 years, 11 months, 30 days

• Research summary

  Lung cancer is the leading cause of death from cancer worldwide. There are many different treatments available for those with lung cancer, and these are often given in combination. This means there are a large number of possible options available to patients, and although research provides good evidence for treatment success across large groups of patients, it is very difficult to know what the best option is for each individual patient.
  To solve this problem, machine learning(ML) (a type of artificial intelligence) has been used in other cancers to identify what treatments would work best for different people, hopefully leading to a better chance of survival.
  This research aims to work towards improving treatment decisions for lung cancer patients by:
  • Understanding what affects lung cancer patient survival and quality of life
  • Developing an online tool that uses ML to accurately predict patient survival and quality of life following differing treatment courses.
  We will review previous research to understand what factors are known to affect quality of life and a persons’ chance of survival if they are treated for lung cancer. The online tool will be generated using techniques such as artificial intelligence (ability of a computer to do human tasks). We will use data collected routinely for patients diagnosed with lung cancer across England and the US to develop and test this tool
  For each patient with lung cancer, the tool will predict the likelihood the patient will be alive in five years’ time and provide an estimate of quality of life, based on treatment choice. By working with professionals and patients, we will start to think of how best to introduce these tools into medical practice, and by allowing the use of these tools to assist when making treatment decisions, this could lead to a better chance of survival

• REC name

  London - London Bridge Research Ethics Committee

• REC reference

  22/PR/1585

• Date of REC Opinion

  7 Dec 2022

• REC opinion

  Favourable Opinion