MyelomaMALIMARNCT03574454
Machine Learning in Myeloma Response
A study testing Machine Learning (ML) in people with Myeloma, run by Royal Marsden NHS Foundation Trust at 3 sites in United Kingdom.
Trial 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. Read more about this study
- Who is it for?
- Ages 40 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Myeloma
- What is being tested?
- Machine Learning (ML)
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 50 participants
- Where
- 3 sitesUnited Kingdom
- Study dates
- 4 Jul 2018to 31 Dec 2022
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 100 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
3 sites in 1 country.
United Kingdom3 sites
Institute of Cancer Research, London
London, SW3 6JB
Imperial College, London
London, W12 0NN
Department of Radiology, The Royal Marsden NHS Foundation Trust
Sutton, Surrey, SM2 5PT
About this study
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
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.
Read the 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:
- Development of the Machine Learning (ML) tool to detect active myeloma
- 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
- 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.
- Conditions
- Myeloma
- Being tested
- Machine Learning (ML)
- Keywords
- Whole Body Diffusion Weighted, Magnetic Resonance Imaging, Machine Learning, Reading Time, Convolutional Neural Network, Algorithm, Diagnostic performance
- Medical subject headings
- Neoplasms, Plasma Cell
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine Learning (ML)Other
Also called Algorithm, Software, Decision support tool, Convolutional neural network
Application of ML support algorithm to accelerate and enhance human interpretation of WB-MRI scans in patients with myeloma
| Study group | Role | What they receive |
|---|---|---|
| Phase 1 - Mixed Scan Data Training Set | Other | 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. Receives: Other: Machine Learning (ML) |
| Phase 2 - Mixed Scan Data Validation Set | Other | 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. Receives: Other: Machine Learning (ML) |
| Phase 3 - Disease Burden Paired Data Set | Other | 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. Receives: Other: Machine Learning (ML) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 20 months
Secondary outcomes (12)
Level of Agreement in Assessment of Disease Burden
Agreement between readers and reference standard in scoring overall disease burden with and without ML intervention
Measured: 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
Measured: 20 months
Quantification of Improvements to Correctly Identify Disease by Site and Reading Time
Per site sensitivity to diagnose active disease
Measured: 20 months
Difference in Reading Time with and without Machine Learning
Difference in reading time assessed in minutes
Measured: 20 months
Specificity for Identification of Active Disease with and without Machine Learning
Per site specificity to diagnose active disease
Measured: 20 months
Sensitivity to detect Active Disease in non-Experienced Readers with and without Machine Learning
Per site sensitivity to diagnose active disease
Measured: 20 months
Agreement in Categorisation of Active Disease
Percentage agreement
Measured: 20 months
Difference in Reading Time for scoring Disease Burden with and without Machine Learning
Difference in reading time assessed in minutes
Measured: 5 months
Agreement in Categorisation of Disease Responders and non-Responders with Reference Standard
Percentage Agreement
Measured: 5 months
Agreement in Categorisation of Disease Responders and non-Responders in non-Experienced Readers
Percentage Agreement
Measured: 5 months
Agreement in Assessment of Disease Burden in non-Experienced Readers
Percentage Agreement
Measured: 5 months
Difference in Costs of Radiology Reading Time with and without Machine Learning
Selected denominations
Measured: 20 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Jul 2018
- Study start4 Jul 2018
- Primary completion31 Aug 2022 (estimated)
- Study completion31 Dec 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for MyelomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Single group
- 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 blind
- Who is masked
- Outcomes assessor
- Enrolment
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Royal Marsden NHS Foundation Trust
- Sponsor type
- University, hospital or other
- Collaborators
- Institute of Cancer Research, United Kingdom, Imperial College London
- Responsible party
- Sponsor
- Investigators
- Andrea G Rockall, FRCR, Study director, The Royal Marsden NHS Foundation Trust and Imperial College LondonChristina Messiou, MD, FRCR, Principal investigator, The Royal Marsden NHS Foundation Trust and Institute of Cancer Research
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03574454
- Sponsor's protocol ID
- CCR 4820
- Other IDs
- 233501, 16/68/34
- Acronym
- MALIMAR
- Status last verified
- Jan 2022
- First submitted
- 1 May 2018
- Last updated
- 11 Jan 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 36198471
Study at a glance
From the registry record, last updated by the sponsor on 11 Jan 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT03574454
- Acronym / sponsor ID
- MALIMAR / CCR 4820
- Conditions
- Myeloma
- Keywords
- Whole Body Diffusion Weighted, Magnetic Resonance Imaging, Machine Learning, Reading Time, Convolutional Neural Network, Algorithm, Diagnostic performance
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 50 participants (estimated)
- Lead sponsor
- Royal Marsden NHS Foundation TrustUniversity, hospital or other
- Collaborators
- Institute of Cancer Research, United Kingdom, Imperial College London
- Study start
- 4 Jul 2018 (actual)
- Primary completion
- 31 Aug 2022 (estimated)
- Study completion
- 31 Dec 2022 (estimated)
- First posted
- 2 Jul 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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:
- Development of the Machine Learning (ML) tool to detect active myeloma
- 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
- 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 design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Single group
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 3
- 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.
| Arm | Type | Receives |
|---|---|---|
| Phase 1 - Mixed Scan Data Training Set | Other | Other: Machine Learning (ML)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. |
| Phase 2 - Mixed Scan Data Validation Set | Other | Other: Machine Learning (ML)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. |
| Phase 3 - Disease Burden Paired Data Set | Other | Other: Machine Learning (ML)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. |
Who may qualify
- Ages
- 40 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Machine Learning (ML)Also: Algorithm, Software, Decision support tool, Convolutional neural networkApplication of ML support algorithm to accelerate and enhance human interpretation of WB-MRI scans in patients with myeloma | Other | Phase 1 - Mixed Scan Data Training Set, Phase 2 - Mixed Scan Data Validation Set, Phase 3 - Disease Burden Paired Data Set |
Primary outcome
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
12 secondary outcomes
Level of Agreement in Assessment of Disease Burden
Time frame: 5 months
Level of Agreement to Classify Disease Spread
Time frame: 20 months
Quantification of Improvements to Correctly Identify Disease by Site and Reading Time
Time frame: 20 months
Difference in Reading Time with and without Machine Learning
Time frame: 20 months
Specificity for Identification of Active Disease with and without Machine Learning
Time frame: 20 months
Sensitivity to detect Active Disease in non-Experienced Readers with and without Machine Learning
Time frame: 20 months
Agreement in Categorisation of Active Disease
Time frame: 20 months
Difference in Reading Time for scoring Disease Burden with and without Machine Learning
Time frame: 5 months
Agreement in Categorisation of Disease Responders and non-Responders with Reference Standard
Time frame: 5 months
Agreement in Categorisation of Disease Responders and non-Responders in non-Experienced Readers
Time frame: 5 months
Agreement in Assessment of Disease Burden in non-Experienced Readers
Time frame: 5 months
Difference in Costs of Radiology Reading Time with and without Machine Learning
Time frame: 20 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Jan 2022.
- Derived from this study: PubMed 36198471Satchwell 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Andrea G Rockall, FRCR study director
The Royal Marsden NHS Foundation Trust and Imperial College London
Christina Messiou, MD, FRCR principal investigator
The Royal Marsden NHS Foundation Trust and Institute of Cancer Research
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Machine Learning (ML) in people with Myeloma, run by Royal Marsden NHS Foundation Trust at 3 sites in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 11 Jan 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of Machine Learning Algorithm to detect Myeloma (time frame: 20 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03574454, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Jan 2022.