Machine Learning in Myeloma Response (MALIMAR)

Development of a Machine Learning Support for Reading Whole Body Diffusion Weighted Magnetic Resonance Imaging (WB-DW-MRI) in Myeloma for the Detection and Quantification of the Extent of Disease Before and After Treatment

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Myeloma
• Intervention / Treatment: Other: Machine Learning (ML)

Detailed Description

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.

• Study Type: Interventional
• Enrollment (Anticipated): 50
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United Kingdom
  • London, United Kingdom, SW3 6JB
    • Institute of Cancer Research, London
  • London, United Kingdom, W12 0NN
    • Imperial College, London
  • Surrey
    • Sutton, Surrey, United Kingdom, SM2 5PT
      • Department of Radiology, The Royal Marsden NHS Foundation Trust

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 40 years to 100 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

Inclusion Criteria (healthy volunteers):

• Able to provide written informed consent
• No contra-indication to MRI
• 40 years or above in age (age matched as far as possible to WB-MRI scan set)
• No known significant illness
• No known metallic implant

Exclusion Criteria:

• Not able to provide written informed consent
• A contra-indication to MRI
• <40 years or above in age (age matched as far as possible to WB-MRI scan set)
• A known significant illness
• A known metallic implant

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Non-Randomized
• Interventional Model: Single Group Assignment
• Masking: Single

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Other: Phase 1 - Mixed Scan Data Training Set; Machine learning (ML): A mixed data set of 200 WB-MRI scans comprising scans obtained from 40 healthy volunteers (scanned for the purposes of the study), 40 previously acquired inactive myeloma WB-MRI scans and 120 previously acquired active myeloma WB-MRI scans, in which machine learning and convolutional neural networks will be trained to recognise healthy marrow, treated inactive previous myeloma and active myeloma. An algorithm will be developed for testing in phase 2.	Other: Machine Learning (ML); Application of ML support algorithm to accelerate and enhance human interpretation of WB-MRI scans in patients with myeloma; Other Names: Algorithm; Software; Decision support tool; Convolutional neural network
Other: Phase 2 - Mixed Scan Data Validation Set; Machine Learning (ML): A mixed data set of 353 WB-MRI scans as that comprising 50 healthy volunteers (scanned for the purposes of the study), and previously acquired scans from 303 myeloma patients, 100 of whom have inactive disease and 203 of whom have active myeloma. The scans will be read by radiologists in random order either with or without the support of for the detection of active myeloma. The diagnostic performance of the radiology reads with or without the machine learning support will be measured against an expert panel reference standard.	Other: Machine Learning (ML); Application of ML support algorithm to accelerate and enhance human interpretation of WB-MRI scans in patients with myeloma; Other Names: Algorithm; Software; Decision support tool; Convolutional neural network
Other: Phase 3 - Disease Burden Paired Data Set; Machine Learning (ML): Approximately 200 paired WB-MRI scans from 100 patients (scanned at baseline with active disease and then post treatment) will be used to develop a machine learning tool to quantify the burden of disease. The machine learning algorithm will then be tested on a further additional set of 60 patients who previously had two WB-MRI scans comprising paired baseline (with active disease) and post treatment scans. The agreement of radiology readers to evaluate the burden of disease will be measured against the reference standard (expert panel) with and without machine learning support.	Other: Machine Learning (ML); Application of ML support algorithm to accelerate and enhance human interpretation of WB-MRI scans in patients with myeloma; Other Names: Algorithm; Software; Decision support tool; Convolutional neural network

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity of Machine Learning Algorithm to detect Myeloma	Sensitivity for the detection of active myeloma on WB-MRI with and without ML support versus the reference standard	20 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Level of Agreement in Assessment of Disease Burden	Agreement between readers and reference standard in scoring overall disease burden with and without ML intervention	5 months
Level of Agreement to Classify Disease Spread	Agreement of machine learning algorithm with reference standard to classify disease spread assessed as percentage accuracy	20 months
Quantification of Improvements to Correctly Identify Disease by Site and Reading Time	Per site sensitivity to diagnose active disease	20 months
Difference in Reading Time with and without Machine Learning	Difference in reading time assessed in minutes	20 months
Specificity for Identification of Active Disease with and without Machine Learning	Per site specificity to diagnose active disease	20 months
Sensitivity to detect Active Disease in non-Experienced Readers with and without Machine Learning	Per site sensitivity to diagnose active disease	20 months
Agreement in Categorisation of Active Disease	Percentage agreement	20 months
Difference in Reading Time for scoring Disease Burden with and without Machine Learning	Difference in reading time assessed in minutes	5 months
Agreement in Categorisation of Disease Responders and non-Responders with Reference Standard	Percentage Agreement	5 months
Agreement in Categorisation of Disease Responders and non-Responders in non-Experienced Readers	Percentage Agreement	5 months
Agreement in Assessment of Disease Burden in non-Experienced Readers	Percentage Agreement	5 months
Difference in Costs of Radiology Reading Time with and without Machine Learning	Selected denominations	20 months

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Predicting Segmentation Performance of the Machine Learning Algorithm	Percentage Agreement	20 months

Collaborators and Investigators

• Sponsor: Royal Marsden NHS Foundation Trust
• Collaborators: Imperial College London; Institute of Cancer Research, United Kingdom
• Investigators: Study Director: Andrea G Rockall, FRCR, The Royal Marsden NHS Foundation Trust and Imperial College London; Principal Investigator: Christina Messiou, MD, FRCR, The Royal Marsden NHS Foundation Trust and Institute of Cancer Research

Publications and helpful links

General Publications

• Satchwell L, Wedlake L, Greenlay E, Li X, Messiou C, Glocker B, Barwick T, Barfoot T, Doran S, Leach MO, Koh DM, Kaiser M, Winzeck S, Qaiser T, Aboagye E, Rockall A. Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study. BMJ Open. 2022 Oct 5;12(10):e067140. doi: 10.1136/bmjopen-2022-067140.

Study record dates

Study Major Dates

• Study Start (Actual): July 4, 2018
• Primary Completion (Anticipated): August 31, 2022
• Study Completion (Anticipated): December 31, 2022

Study Registration Dates

• First Submitted: May 1, 2018
• First Submitted That Met QC Criteria: June 28, 2018
• First Posted (Actual): July 2, 2018

Study Record Updates

• Last Update Posted (Actual): January 11, 2022
• Last Update Submitted That Met QC Criteria: January 7, 2022
• Last Verified: January 1, 2022

More Information

Terms related to this study

• Keywords: Magnetic Resonance Imaging; Machine Learning; Algorithm; Diagnostic performance; Convolutional Neural Network; Whole Body Diffusion Weighted; Reading Time
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Vascular Diseases; Immune System Diseases; Neoplasms by Histologic Type; Neoplasms; Lymphoproliferative Disorders; Immunoproliferative Disorders; Hematologic Diseases; Hemorrhagic Disorders; Hemostatic Disorders; Paraproteinemias; Blood Protein Disorders; Multiple Myeloma; Neoplasms, Plasma Cell
• Other Study ID Numbers: CCR 4820; 233501 (Other Identifier: Integrated Research Application System (IRAS)); 16/68/34 (Other Grant/Funding Number: National Institute for Health Research (NIHR))

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No