MAchine Learning in whole Body Oncology

ISRCTN	ISRCTN23068310
DOI	https://doi.org/10.1186/ISRCTN23068310
Protocol serial number	C/32/2014
Sponsor	Imperial College, London - Joint Research Compliance Office
Funder	National Institute for Health Research

Submission date: 08/07/2015
Registration date: 28/08/2015
Last edited: 02/09/2025

Recruitment status: No longer recruiting
Overall study status: Completed
Condition category: Cancer

Prospectively registered

Protocol

Statistical analysis plan

Results

Individual participant data

Plain English summary of protocol

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

Contact information

Prof Andrea Rockall
Scientific

Department of Radiology
Imperial College NHS Healthcare Trust
Hammersmith Hospital Campus
London
W12 0NN
United Kingdom

Phone	+44 (0)786 658 5476
Email	a.rockall@imperial.ac.uk

Study information

Primary study design	Observational
Study design	Observational study (study limited to working with data)
Secondary study design	Study limited to working with data
Scientific title	Development and evaluation of machine learning methods in whole body MR with diffusion weighted imaging for staging of patients with cancer
Study acronym	MA.L.I.B.O.
Study objectives	The use of machine learning can improve the diagnostic performance of whole body MRI.
Ethics approval(s)	IC REC for Phases 2 and 3: 15IC2647
Health condition(s) or problem(s) studied	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
Primary outcome measure(s)	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,
Key secondary outcome measure(s)	1. Inter-observer agreement for MR radiologists 2. Evaluation of different MRI sequences on diagnostic accuracy 3. Simulated effect of MRI on clinical management
Completion date	30/07/2018

Eligibility

Participant type(s)	Patient
Age group	All
Sex	All
Target sample size at registration	217
Total final enrolment	438
Key inclusion criteria	As per each source (contributing) study (http://www.isrctn.com/ISRCTN50436483, http://www.isrctn.com/ISRCTN43958015, https://clinicaltrials.gov/ct2/show/NCT01459224)
Key exclusion criteria	As per each source (contributing) study (http://www.isrctn.com/ISRCTN50436483, http://www.isrctn.com/ISRCTN43958015, https://clinicaltrials.gov/ct2/show/NCT01459224)
Date of first enrolment	01/10/2015
Date of final enrolment	30/09/2019

Locations

Countries of recruitment

United Kingdom
England

Study participating centre

Imperial College, London - Hammersmith Campus

W12 0NN
United Kingdom

Results and Publications

Individual participant data (IPD) Intention to share	Yes
IPD sharing plan summary	Stored in repository
IPD sharing plan

Study outputs

Output type	Details	Date created	Date added	Peer reviewed?	Patient-facing?
Results article		26/06/2023	05/09/2023	Yes	No
Results article		01/10/2024	02/09/2025	Yes	No

Editorial Notes

02/09/2025: Publication reference and total final enrolment added.
05/09/2023: Publication reference added.
14/09/2018: The recruitment end date has been changed from 30/07/2018 to 30/09/2019.