Correlate quantitative CT features with long-term outcomes in patients with early signs of fibrosis

Principal Investigator

Name
Joseph Jacob

Degrees
F.R.C.R., M.D.(Res).,

Institution
University College London

Position Title
Wellcome Trust Fellow

Email
j.jacob@ucl.ac.uk

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-543

Initial CDAS Request Approval
Jul 19, 2019

Title
Correlate quantitative CT features with long-term outcomes in patients with early signs of fibrosis

Summary
Idiopathic pulmonary fibrosis (IPF), the most common fibrosining lung disease (FLD) has a median patient survival of 3-5 years. The prevalence of FLD is increasing in the Western world, partly due to ageing within the general population and partly because of increased awareness of the disease. Unfortunately FLD is typically diagnosed at an advanced stage once patients are already physically impaired and patient decline is often rapid.

Diagnosis in turn is often delayed as CT appearances and lung function test profiles are only well characterized for established, relatively late-stage disease. Recognising FLD at an early stage is therefore essential to create a treatment window where disease that has not yet become extensive could potentially be controlled. We aim to use deep learning-based algorithms to quantify lung damage and disease progression and to correlate these quantitative features and signatures with long-term outcomes.

Aims

Correlate quantitative CT features suggestive of early fibrosis with long-term outcomes
Correlate quantitative CT features indicating rapidly progressive fibrosis with long-term outcomes

Collaborators

Prof Daniel Alexander, UCL
Prof Geoff Parker, UCL
Mr Moucheng Xu, UCL
Dr Cheung Wing Keung, UCL
Mr Ashkan Pakzad, UCL
Dr Arjun Nair, UCL
Dr Eyjolfur Gudmundsson, UCL