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Machine Learning in Myeloma Response (MALIMAR)

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ClinicalTrials.gov Identifier: NCT03574454
Recruitment Status : Recruiting
First Posted : July 2, 2018
Last Update Posted : July 17, 2018
Sponsor:
Collaborators:
Institute of Cancer Research, United Kingdom
Imperial College London
Information provided by (Responsible Party):
Royal Marsden NHS Foundation Trust

Brief Summary:

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.


Condition or disease Intervention/treatment Phase
Myeloma Other: Machine Learning (ML) Not Applicable

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.


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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 50 participants
Allocation: Non-Randomized
Intervention Model: Single Group Assignment
Intervention Model Description: Cross-sectional diagnostic test accuracy design: development of a machine-based algorithm to augment expert classification of disease status and response to treatment in myeloma patients using retrospective interpretation of WB-MRI scans and disease-free (healthy volunteers) for comparison purposes.
Masking: Single (Outcomes Assessor)
Masking Description: The assessors interpretation of disease status using WB-MRI scans will be fully blinded to the reference standard (i.e. the Expert Panel's interpretation of the same scan).
Primary Purpose: Diagnostic
Official Title: 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
Actual Study Start Date : July 4, 2018
Estimated Primary Completion Date : May 2021
Estimated Study Completion Date : August 2021

Resource links provided by the National Library of Medicine


Arm Intervention/treatment
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

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

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




Primary Outcome Measures :
  1. Sensitivity of Machine Learning Algorithm to detect Myeloma [ Time Frame: 20 months ]
    Sensitivity for the detection of active myeloma on WB-MRI with and without ML support versus the reference standard


Secondary Outcome Measures :
  1. Level of Agreement in Assessment of Disease Burden [ Time Frame: 5 months ]
    Agreement between readers and reference standard in scoring overall disease burden with and without ML intervention

  2. Level of Agreement to Classify Disease Spread [ Time Frame: 20 months ]
    Agreement of machine learning algorithm with reference standard to classify disease spread assessed as percentage accuracy

  3. Quantification of Improvements to Correctly Identify Disease by Site and Reading Time [ Time Frame: 20 months ]
    Per site sensitivity to diagnose active disease

  4. Difference in Reading Time with and without Machine Learning [ Time Frame: 20 months ]
    Difference in reading time assessed in minutes

  5. Specificity for Identification of Active Disease with and without Machine Learning [ Time Frame: 20 months ]
    Per site specificity to diagnose active disease

  6. Sensitivity to detect Active Disease in non-Experienced Readers with and without Machine Learning [ Time Frame: 20 months ]
    Per site sensitivity to diagnose active disease

  7. Agreement in Categorisation of Active Disease [ Time Frame: 20 months ]
    Percentage agreement

  8. Difference in Reading Time for scoring Disease Burden with and without Machine Learning [ Time Frame: 5 months ]
    Difference in reading time assessed in minutes

  9. Agreement in Categorisation of Disease Responders and non-Responders with Reference Standard [ Time Frame: 5 months ]
    Percentage Agreement

  10. Agreement in Categorisation of Disease Responders and non-Responders in non-Experienced Readers [ Time Frame: 5 months ]
    Percentage Agreement

  11. Agreement in Assessment of Disease Burden in non-Experienced Readers [ Time Frame: 5 months ]
    Percentage Agreement

  12. Difference in Costs of Radiology Reading Time with and without Machine Learning [ Time Frame: 20 months ]
    Selected denominations


Other Outcome Measures:
  1. Predicting Segmentation Performance of the Machine Learning Algorithm [ Time Frame: 20 months ]
    Percentage Agreement



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   40 Years to 100 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Criteria

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03574454


Contacts
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Contact: Linda J Wedlake, PhD +44 208 915 6767 ext 6767 linda.wedlake@rmh.nhs.uk
Contact: Jeane Guevara, BSc + 44 208 915 6666 ext 6666 jeane.guevara@rmh.nhs.uk

Locations
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United Kingdom
Department of Radiology, The Royal Marsden NHS Foundation Trust Recruiting
Sutton, Surrey, United Kingdom, SM2 5PT
Contact: Christina Messiou, PhD    02086426011    Christina.Messiou@rmh.nhs.uk   
Sub-Investigator: Dow-Mu Koh, PhD         
Sub-Investigator: Karen Thomas, BSc         
Institute of Cancer Research, London Recruiting
London, United Kingdom, SW3 6JB
Contact: Martin Leach, PhD       Martin.Leach@icr.ac.uk   
Contact: Melisa Porter    0208 661 3701    melisa.porter@icr.ac.uk   
Sub-Investigator: Martin Kaiser, MD         
Sub-Investigator: Simon Doran, PhD         
Imperial College, London Recruiting
London, United Kingdom, W12 0NN
Contact: Daniel Ruekert, PhD       d.rueckert@imperial.ac.uk   
Contact: Ben Glocker, PhD       b.glocker@imperial.ac.uk   
Sub-Investigator: Eric Aboagye, PhD         
Sub-Investigator: Tara Barwick, PhD         
Sponsors and Collaborators
Royal Marsden NHS Foundation Trust
Institute of Cancer Research, United Kingdom
Imperial College London
Investigators
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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

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Responsible Party: Royal Marsden NHS Foundation Trust
ClinicalTrials.gov Identifier: NCT03574454     History of Changes
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) )
First Posted: July 2, 2018    Key Record Dates
Last Update Posted: July 17, 2018
Last Verified: July 2018
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Keywords provided by Royal Marsden NHS Foundation Trust:
Whole Body Diffusion Weighted
Magnetic Resonance Imaging
Machine Learning
Reading Time
Convolutional Neural Network
Algorithm
Diagnostic performance

Additional relevant MeSH terms:
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Multiple Myeloma
Neoplasms, Plasma Cell
Neoplasms by Histologic Type
Neoplasms
Hemostatic Disorders
Vascular Diseases
Cardiovascular Diseases
Paraproteinemias
Blood Protein Disorders
Hematologic Diseases
Hemorrhagic Disorders
Lymphoproliferative Disorders
Immunoproliferative Disorders
Immune System Diseases