MALIMAR - MAchine Learning In MyelomA Response
Research type
Research Study
Full title
Development of a machine learning support for reading whole body diffusion weighted magnetic resonance imaging (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment.
IRAS ID
233501
Contact name
Andrea Rockall
Contact email
Sponsor organisation
The Royal Marsden NHS Foundation Trust
Duration of Study in the UK
3 years, 2 months, 30 days
Research summary
Diffusion-weighted whole body magnetic resonance imaging (WB-MRI) is a new technique that builds on existing MRI technology. It uses the movement of water molecules in human tissue to define with great accuracy cancerous cells from normal cells. Using this technique we can much more accurately define the spread and rate of cancer growth. This information is vital in the selection of patients’ treatment pathways. WB-MRI images are obtained for the entire body in a single scan. Unlike other imaging techniques such as CT or PET/CT there is no radiation exposure.
Despite the considerable advantages that this new technique brings, including “at a glance” assessment of the extent of disease status, WB-MRI requires a significant increase in the time required to interpret one scan. This is because one whole body scan typically comprises several thousand images. Machine learning (ML) is a computer technique in which computers can be ‘trained’ to rapidly pin-point sites of disease and thus aid the radiologist’s expert interpretation. If, as we believe, this technique will help the radiologist to interpret scans of patients with myeloma more accurately and quickly, it could be more widely adopted by the NHS and benefit patient care.We propose a research plan in which ML software will be developed and tested with the aim of achieving more rapid and accurate interpretation of WB-MRI scans in myeloma patients.
Research will be carried out in the Royal Marsden NHS Foundation Trust and Imperial College London. Work will be divided into three phases: 1) development of the ML software tool to detect active myeloma; 2) measurement of the improvements made to the radiologists’ interpretation of WB-MRI using scans from patients with active and inactive myeloma and scans from healthy volunteers as a comparator; 3) development of the ML tool to ensure it is capable of identifying the degree of response to treatment.
REC name
South Central - Oxford C Research Ethics Committee
REC reference
17/SC/0630
Date of REC Opinion
17 Nov 2017
REC opinion
Favourable Opinion