Predicting the location and interval of lung nodule occurrence from low-dose CT
Principal Investigator
Name
Matthew Blackledge
Degrees
Ph.D, MSc, BSc
Institution
Institute of Cancer Research
Position Title
Team Leader
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-493
Initial CDAS Request Approval
Apr 4, 2019
Title
Predicting the location and interval of lung nodule occurrence from low-dose CT
Summary
The potential benefits of lung cancer screening have been re-rejuvenated by evolving data from the NELSON study. Large roll-out of national pilots in the UK are planned. Optimal screening interval and risk stratification for personalized surveillance are poorly understood.
Our aim is to develop and test a novel artificial intelligence (AI) architecture that combines baseline LDCT studies with other patient information such as age, smoking status, and smoking history (patient demographics) to identify the most relevant data for personalized screening intervals for patients at risk of developing lung cancer.
Our hypothesis is that apparently "normal‟ baseline LDCT scans will have certain features, perhaps not visible to the human eye, that can predict time-to-lesion-appearance of lung tumours.
Our aim is to develop and test a novel artificial intelligence (AI) architecture that combines baseline LDCT studies with other patient information such as age, smoking status, and smoking history (patient demographics) to identify the most relevant data for personalized screening intervals for patients at risk of developing lung cancer.
Our hypothesis is that apparently "normal‟ baseline LDCT scans will have certain features, perhaps not visible to the human eye, that can predict time-to-lesion-appearance of lung tumours.
Aims
We aim to identify and model a pre-cancerous signature present on baseline LDCT lung scans using our technology. Using these data, we will train a novel end-to-end deep-learning network using state-of-the-art high-performance computing, and validate our model on an independent test set randomly sampled from the available data.
Collaborators
Professor Xujiong Ye - University of Lincoln
Dr Reyer Zwiggelaar - University of Aberystwyth
Dr Spencer Thomas - NPL Management Ltd
Dr Balaji Ganeshan - University College London
Dr Richard Lee - Royal Marsden Hospital
Dr Carolyn Horst - University College London