Machine Learning in Myeloma Response

Overview

Diffusion-weighted Whole Body Magnetic Resonance Imaging (WB-MRI) is a new technique that builds on existing Magnetic Resonance Imaging (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 the investigators 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 computed Tomography (CT) or Positron Emission Tomography (PET) 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 the investigators 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. The investigators will conduct a three-phase 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.

Rationale: Diffusion-weighted whole body magnetic resonance imaging (WB-MRI) is a technique that depicts myeloma deposits in the bone marrow. WB-MRI covers the entire body during the course of a single scan and can be used to detect sites of disease without using ionising radiation. Although WB-MRI allows for "at a glance" assessment of disease burden, it requires significant expertise to accurately identify and quantify active myeloma. The technique is time-consuming to report due to the great number of images. A further challenge is recognising whether a patient has residual disease after treatment. Machine learning (ML) is a computer technique that can be trained to automatically detect disease sites in order to support the radiologist's interpretation. The investigators believe this technique will help the radiologist to interpret the scan more accurately and quickly. Machine learning algorithms have been successfully developed to recognise some other cancer types. The investigators believe that it may be successful in patients with myeloma, in whom The National Institute for Health and Care Excellence (NICE) recommend whole body MRI. This could allow the technique to be more widely used in the National Health Service (NHS). In the MALIMAR study the investigators will develop and test ML methods that have the potential to increase accuracy and reduce reading time of WB-MRI scans in myeloma patients. The investigators propose to develop ML tools to detect and quantify active disease before and after treatment based on WB-MRI. Research will be carried out at the Royal Marsden Hospital (RMH) NHS Foundation Trust, Institute of Cancer Research (ICR) London and Imperial College London. The investigators will use Whole Body MRI (WB-MRI) scans that have already been acquired in myeloma patients. They will also include 50 new scans obtained at RMH from healthy volunteer scans which will be used to 'teach' the computer to distinguish between healthy and diseased tissues. Research Design: The research will be divided into three parts: 1. Development of the Machine Learning (ML) tool to detect active myeloma 2. Measurement of the ability of the ML tool to improve the radiologists' interpretation of WB-MRI scans using a set of scans from patients with active and inactive myeloma and new scans obtained from healthy volunteers 3. Development of the ML tool to quantify disease burden and changes between pre- and post-treatment WB-MRI scans in order to identify response to treatment The main outcome measure for this study will be the improvement in the detection of active disease and disease burden and the reduction in radiology reading time. The investigators will assess the reduction in reading time in both experienced specialist and non-specialist radiologists.