Image based prediction of aggressive early lung cancer in lung cancer screening populations at UCL

Project title: Image based prediction of aggressive early lung cancer in lung cancer screening populations.

Supervision team:

Professor Daniel Alexander

Dr Joseph Jacob

Dr Adam Pennycuick

A 4-year funded PhD studentship is available in the Centre for Medical Image Computing at UCL:

The project will be based in the Satsuma Research Group

Funding will be at least the UCL minimum. Stipend details can be found here.

The successful candidate will join the UCL CDT in Intelligent, Integrated Imaging in Healthcare (i4health) cohort and benefit from the activities and events organised by the centre.

A full studentship is available for home fee payers only. UCL’s fee eligibility criteria can a be found by following this link.

Project Background:

Lung cancer screening invites high risk subjects to have a CT scan of their lungs to identify early treatable lung cancer. By 2028 approximately 500-750,000 subjects will have a CT scan of their lungs annually in the National UK lung cancer screening program. 2-3% of screened subjects will have a lung cancer. Lung cancers can show differing rates of growth and spread to lymph nodes, and some lung cancers, despite treatment can recur. Identifying potentially aggressive lung cancers at an early stage could transform lung cancer management worldwide. Cancers expected to be aggressive could be treatment with extra chemotherapy prior to surgery.

Our study will analyse data from two UCL studies: SUMMIT and ASCENT. The SUMMIT study is one of the largest lung cancer screening studies in the world which has scanned >13,000 subjects annually to identify lung cancer. The ASCENT study comprises all SUMMIT study patients where a lung cancer was diagnosed. The cancers in the ASCENT study have been genotyped and have longitudinal outcome data collected.

This study aims to correlate imaging features of lung cancer growth with clinical and genomic mutational markers of aggression.

Research Aims:

• Identify image-based features of malignant lung nodules on low-dose CT scans that predict aggressive disease.
• Evaluate mediastinal lymph node change as a predictor of aggressive disease.
• Identify genomic signatures on imaging data that can predict aggressive disease.

Person specification & requirements:

• Undergraduate degree of 2:1 or higher
• Previous experience in working with medical imaging.
• Proficient in the use of deep learning models
• Fluent in python and pytorch

Application deadline 30^th June 2023.

How to Apply

Please complete the following steps to apply.

• Send an expression of interest and current CV to: [j.jacob@ucl.ac.uk and cdtadmin@ucl.ac.uk] Please quote Project Code:23025 in the email subject line.
• Make a formal application to via the UCL application portal . Please select the programme code Medical Imaging TMRMEISING01 and enter Project Code 23025 under ‘Name of Award 1’.