Image based prediction of aggressive early lung cancer in lung cancer screening populations
Lead Research Organisation:
University College London
Department Name: Medical Physics and Biomedical Eng
Abstract
1) Brief description of the context of the research including potential impact
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.
2) Aims and Objectives
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.
3) Novelty of Research Methodology
Defining aggression in early lung cancer. Aggression currently has no formal medical definition, but creating this could be valuable for many cancer types
Use of time-series deep learning models on medical imaging considering lung and extra-lung features to predict disease progression
Novel histopathological-genomic-imaging correlations to delineate a multidimensional risk score predictive of aggressive early lung cancer
4) Alignment to EPSRC's strategies and research areas
EPSRC Strategic Priorities: transforming health and healthcare
EPSRC Research Portfolio and Priorities: healthcare technologies
5) Any companies or collaborators involved
N/A
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.
2) Aims and Objectives
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.
3) Novelty of Research Methodology
Defining aggression in early lung cancer. Aggression currently has no formal medical definition, but creating this could be valuable for many cancer types
Use of time-series deep learning models on medical imaging considering lung and extra-lung features to predict disease progression
Novel histopathological-genomic-imaging correlations to delineate a multidimensional risk score predictive of aggressive early lung cancer
4) Alignment to EPSRC's strategies and research areas
EPSRC Strategic Priorities: transforming health and healthcare
EPSRC Research Portfolio and Priorities: healthcare technologies
5) Any companies or collaborators involved
N/A
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/S021930/1 | 01/10/2019 | 31/03/2028 | |||
| 2876044 | Studentship | EP/S021930/1 | 01/10/2023 | 30/09/2027 | Helen McGregor |