Condition category
Cancer
Date applied
08/07/2015
Date assigned
28/08/2015
Last edited
28/08/2015
Prospective/Retrospective
Prospectively registered
Overall trial status
Ongoing
Recruitment status
Recruiting

Plain English Summary

Background and study aims
Magnetic resonance imaging (MRI) is a type of scan that produces detailed images of the inside of the body. Whole body magnetic resonance imaging (MRI) is being increasingly used to assess the amount of tumours in patients with metastatic cancer (cancer that has spread). However, whole body MRI has limitations in its diagnostic performance. Moreover, it is time-consuming to report a whole body scan, even for experienced readers. Machine learning is the process of training a computer to make decisions, based on already existing data. The aim of this study is to develop and test machine learning methods, applied to whole body MRI, to improve the diagnostic performance of whole body MRI and reduce the amount of time it takes for a radiologist to read a whole body MRI scan.

Who can participate?
This is a study limited to working with data from completed or currently recruiting studies. The study is divided into three Phases. In Phase 1 whole body MRI scans from healthy adult volunteers will be used. In Phases 2 and 3 data from child and adult patients with metastatic cancer will be used.

What does the study involve?
The MRI scans’ diagnostic performance and radiologists’ reading time will be compared with and without the assistance of the developed machine learning methods.

What are the possible benefits and risks of participating?
There are no perceived risks or direct benefits to the participants, as the application of the machine learning methods will take place after their clinical imaging has taken place. Future potential benefits include improvements in the diagnostic accuracy of whole body MRI scans, which could improve the initial cancer staging process, by improving the accurate detection of metastatic disease and reducing staging errors that could lead to unnecessary tests. Undergoing a single highly effective test for staging would also avoid the need for multiple patient visits to hospital.

Where is the study run from?
The study is run and led by the Imperial College Comprehensive Cancer Imaging Centre (C.C.I.C.) and Department of Computing. Participating centres (providing imaging data and radiological reading support) include the following: University College London (UCL) and UCL Hospital (UCLH), King’s College London and the Royal Marsden Hospital (RMH).

When is the study starting and how long is it expected to run for?
From February 2015 to July 2018.

Who is funding the study?
National Institute of Health Research (UK).

Who is the main contact?
Prof Andrea G. Rockall
a.rockall@imperial.ac.uk

Trial website

Contact information

Type

Scientific

Primary contact

Prof Andrea Rockall

ORCID ID

Contact details

Department of Radiology
Imperial College NHS Healthcare Trust
Hammersmith Hospital Campus
London
W12 0NN
United Kingdom
+44 (0)786 658 5476
a.rockall@imperial.ac.uk

Additional identifiers

EudraCT number

ClinicalTrials.gov number

Protocol/serial number

C/32/2014

Study information

Scientific title

Development and evaluation of machine learning methods in whole body MR with diffusion weighted imaging for staging of patients with cancer

Acronym

MA.L.I.B.O.

Study hypothesis

The use of machine learning can improve the diagnostic performance of whole body MRI.

Ethics approval

IC REC for Phases 2 and 3: 15IC2647

Study design

Observational study (study limited to working with data)

Primary study design

Observational

Secondary study design

Study limited to working with data

Trial setting

Other

Trial type

Diagnostic

Patient information sheet

Condition

Cancer

Intervention

Research will be carried out at Imperial College London in collaboration with the teams of main studies (NIHR STREAMLINE (colon & lung cancer patients) and CRUK MELT (lymphoma patients)) who have recruited all the patients for diffusion weighted imaging (WB-DW-MR) datasets. The proposed study has three stages: firstly, WB-DW-MR from 50 healthy volunteers will be used to develop the machine learning (ML) method for automatic recognition of normal appearances. Secondly, the ML method will be tested on 150 WB-MR scans from the main studies, in whom the different sites of disease have already been confirmed. The ML method will be refined by radiologists who will identify the correct sites of disease and find ML errors. This will be used to improve the ML method. Thirdly, the refined ML method will be tested in a second group of 169 patients from the main studies to see if the technique can improve radiology reporting by improving diagnostic accuracy (DA) and reading time (RT).

Intervention type

Other

Phase

Drug names

Primary outcome measures

Per site sensitivity and specificity of MRI for nodal and extra-nodal sites and concordance in final disease stage with the multi-modality reference standard (at staging). The reference standard for the MELT study is contemporaneous MDT with all other staging eg PET CT and CT at the time of diagnosis and initial staging,

Secondary outcome measures

1. Inter-observer agreement for MR radiologists
2. Evaluation of different MRI sequences on diagnostic accuracy
3. Simulated effect of MRI on clinical management

Overall trial start date

01/02/2015

Overall trial end date

30/07/2018

Reason abandoned

Eligibility

Participant inclusion criteria

As per each source (contributing) study (http://www.isrctn.com/ISRCTN50436483, http://www.isrctn.com/ISRCTN43958015, https://clinicaltrials.gov/ct2/show/NCT01459224)

Participant type

Patient

Age group

All

Gender

Both

Target number of participants

217

Participant exclusion criteria

As per each source (contributing) study (http://www.isrctn.com/ISRCTN50436483, http://www.isrctn.com/ISRCTN43958015, https://clinicaltrials.gov/ct2/show/NCT01459224)

Recruitment start date

01/10/2015

Recruitment end date

30/07/2018

Locations

Countries of recruitment

United Kingdom

Trial participating centre

Imperial College, London - Hammersmith Campus
W12 0NN
United Kingdom

Sponsor information

Organisation

Imperial College, London - Joint Research Compliance Office

Sponsor details

5th Floor Lab Block
Charing Cross Hospital
Fulham Palace Road
London
W6 8RF
United Kingdom
+44 (0)20 3311 0205
becky.ward@imperial.ac.uk

Sponsor type

University/education

Website

Funders

Funder type

Government

Funder name

National Institute for Health Research

Alternative name(s)

NIHR

Funding Body Type

government organisation

Funding Body Subtype

Federal/National Government

Location

United Kingdom

Results and Publications

Publication and dissemination plan

We plan to disseminate our study findings in the appropriate conventional, peer-reviewed conference presentations, including high impact radiology, computing sciences, MR physics and clinical conferences (in abstract form as scientific presentations at radiology conferences, as well as computing sciences conferences) and as full original scientific peer-reviewed publications in high impact factor scientific journals. The co-applicants have significant experience in presenting and publishing in their own fields.The study findings may have an important impact on the use of WB-DW-MRI in cancer and the subsequent patient management and therefore we intend to present this information to the appropriate forum at the Royal Colleges of Radiologists, International Cancer Imaging Society and MIUA (Medical Image Understanding and Analysis). We will present the findings at national cancer meetings, including the NCRI conference. We hope to have scientific abstracts accepted for publication at the MICCAI (Medical Image Computing and Computer Assisted Intervention) and the clinical imaging conferences ISMRM (International Society of Magnetic Resonance in Medicine) and RSNA (Radiological Society of North America). Dissemination of the project outcomes will be vital to the NHS and public organisations who may be able to benefit. We will use link with national cancer networks and disseminate findings through any relevant NHS clinical care groupings.

Intention to publish date

Participant level data

Stored in repository

Results - basic reporting

Publication summary

Publication citations

Additional files

Editorial Notes