Machine Learning in Myeloma Response

Myeloma Clinical Trial

— MALIMAR  

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT03574454
• Other study ID #: CCR 4820
• Secondary ID: 23350116/68/34
• Status: Active, not recruiting
• Phase: N/A
• Start date: July 4, 2018
• Est. completion date: December 31, 2022

Study information

• Verified date: January 2022
• Source: Royal Marsden NHS Foundation Trust
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical 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. 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.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 50
• Est. completion date: December 31, 2022
• Est. primary completion date: August 31, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 40 Years to 100 Years

Eligibility

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

Related Conditions & MeSH terms

• Multiple Myeloma
• Myeloma
• Neoplasms, Plasma Cell

Intervention

Other:
• Machine Learning (ML)
Application of ML support algorithm to accelerate and enhance human interpretation of WB-MRI scans in patients with myeloma

Locations

Country	Name	City	State
United Kingdom	Imperial College, London	London
United Kingdom	Institute of Cancer Research, London	London
United Kingdom	Department of Radiology, The Royal Marsden NHS Foundation Trust	Sutton	Surrey

Sponsors (3)

Lead Sponsor	Collaborator
Royal Marsden NHS Foundation Trust	Imperial College London, Institute of Cancer Research, United Kingdom

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Predicting Segmentation Performance of the Machine Learning Algorithm	Percentage Agreement	20 months
Primary	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	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
Secondary	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
Secondary	Quantification of Improvements to Correctly Identify Disease by Site and Reading Time	Per site sensitivity to diagnose active disease	20 months
Secondary	Difference in Reading Time with and without Machine Learning	Difference in reading time assessed in minutes	20 months
Secondary	Specificity for Identification of Active Disease with and without Machine Learning	Per site specificity to diagnose active disease	20 months
Secondary	Sensitivity to detect Active Disease in non-Experienced Readers with and without Machine Learning	Per site sensitivity to diagnose active disease	20 months
Secondary	Agreement in Categorisation of Active Disease	Percentage agreement	20 months
Secondary	Difference in Reading Time for scoring Disease Burden with and without Machine Learning	Difference in reading time assessed in minutes	5 months
Secondary	Agreement in Categorisation of Disease Responders and non-Responders with Reference Standard	Percentage Agreement	5 months
Secondary	Agreement in Categorisation of Disease Responders and non-Responders in non-Experienced Readers	Percentage Agreement	5 months
Secondary	Agreement in Assessment of Disease Burden in non-Experienced Readers	Percentage Agreement	5 months
Secondary	Difference in Costs of Radiology Reading Time with and without Machine Learning	Selected denominations	20 months