Machine Learning in Myeloma Response

NCT03574454 | Unknown DMC

• 3 other identifiers: CCR 482023350116/68/34
• Study Type: interventional
• Target: 50
• Locations: 1 country
• Sites: 3

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.

Conditions

Myeloma

Keywords

Whole Body Diffusion Weighted; Magnetic Resonance Imaging; Machine Learning; Reading Time; Convolutional Neural Network; Algorithm; Diagnostic performance

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (12)

• Level of Agreement in Assessment of Disease Burden

  5 months

• Level of Agreement to Classify Disease Spread

  20 months

• Quantification of Improvements to Correctly Identify Disease by Site and Reading Time

  20 months

• Difference in Reading Time with and without Machine Learning

  20 months

• Specificity for Identification of Active Disease with and without Machine Learning

  20 months

Other Outcomes (1)

• Predicting Segmentation Performance of the Machine Learning Algorithm

  20 months

Study Arms (3)

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.

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.

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.

Other: Machine Learning (ML)

Interventions

Machine Learning (ML) OTHER

Application of ML support algorithm to accelerate and enhance human interpretation of WB-MRI scans in patients with myeloma

Also known as: Algorithm, Software, Decision support tool, Convolutional neural network

Phase 1 - Mixed Scan Data Training Set; Phase 2 - Mixed Scan Data Validation Set; Phase 3 - Disease Burden Paired Data Set

Eligibility Criteria

• Age: 40 Years - 100 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

• 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

Sponsors & Collaborators

• Royal Marsden NHS Foundation Trust: lead
• Institute of Cancer Research, United Kingdom: collaborator
• Imperial College London: collaborator

Study Sites (3)

Department of Radiology, The Royal Marsden NHS Foundation Trust

Sutton, Surrey, SM2 5PT, United Kingdom

Location

Institute of Cancer Research, London

London, SW3 6JB, United Kingdom

Location

Imperial College, London

London, W12 0NN, United Kingdom

Location

Related Publications (1)

• 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.

  PMID: 36198471 DERIVED

MeSH Terms

Conditions

Neoplasms, Plasma Cell

Interventions

Machine Learning; Algorithms; Convolutional Neural Networks

Condition Hierarchy (Ancestors)

Neoplasms by Histologic Type; Neoplasms

Intervention Hierarchy (Ancestors)

Artificial Intelligence; Mathematical Concepts; Neural Networks, Computer

Study Officials

• Andrea G Rockall, FRCR

  The Royal Marsden NHS Foundation Trust and Imperial College London

  STUDY DIRECTOR

• Christina Messiou, MD, FRCR

  The Royal Marsden NHS Foundation Trust and Institute of Cancer Research

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Masking Details: 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).
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: 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.

Study Record Dates

• First Submitted: May 1, 2018
• First Posted: July 2, 2018
• Study Start: July 4, 2018
• Primary Completion: August 31, 2022
• Study Completion: December 31, 2022
• Last Updated: January 11, 2022

Record last verified: 2022-01

Data Sharing

• IPD Sharing: Will not share

Locations

GB (3)