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Machine Learning in Myeloma Response

NCT03574454 · Status: UNKNOWN · Phase: NA · Type: INTERVENTIONAL · Enrollment: 50

Last updated 2022-01-11

No results posted yet for this study

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

Interventions

OTHER

Machine Learning (ML)

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

Sponsors & Collaborators

  • Institute of Cancer Research, United Kingdom

    collaborator OTHER

  • Imperial College London

    collaborator OTHER

  • Royal Marsden NHS Foundation Trust

    lead OTHER

Principal Investigators

  • Andrea G Rockall, FRCR · The Royal Marsden NHS Foundation Trust and Imperial College London

  • Christina Messiou, MD, FRCR · The Royal Marsden NHS Foundation Trust and Institute of Cancer Research

Study Design

Allocation
NON_RANDOMIZED
Purpose
DIAGNOSTIC
Masking
SINGLE
Model
SINGLE_GROUP

Eligibility

Min Age
40 Years
Max Age
100 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2018-07-04
Primary Completion
2022-08-31
Completion
2022-12-31

Countries

  • United Kingdom

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03574454 on ClinicalTrials.gov