Artificial intelligence and big data for early lung cancer diagnosis prospective study (phase 2)
This study primarily aims to determine the overall diagnostic performance of a new computer aided prediction (CAP) model for malignancy in small pulmonary nodules.
The study participants are patients with small pulmonary nodule(s) (5–15mm) detected on CT chest scan who have been referred to the lung nodule clinic or for assessment to the specialist referral centre.
The health economic analyses for this study aim to examine the impact of CAP by comparing with the usual care in terms of both health care and societal costs and quality of life of the participants included in the study cohort.
This will be a within-study cost effectiveness analysis using a decision tree model where the end points are final diagnosis of lung cancer.
Planned data collections include the EQ-5D and an anxiety questionnaire completed at baseline and regular follow-up intervals.
Oxford project lead:
PI: Professor Fergus Gleeson (Oxford University NHS trust)
Colleagues from the Oxford Radiology Research Unit, Churchill Hospital and the Oxford Respiratory Trials Unit.
2018 – 2022
National Institute for Health Research (Invention for Innovation)