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Trial registered on ANZCTR


Registration number
ACTRN12624000309583
Ethics application status
Approved
Date submitted
19/10/2023
Date registered
22/03/2024
Date last updated
8/09/2024
Date data sharing statement initially provided
22/03/2024
Type of registration
Prospectively registered

Titles & IDs
Public title
VIBRANT: Targeting Variations in gastroIntestinal cancer Biology with MRI and radiotherapy using Artificial iNTelligence
Scientific title
VIBRANT: Targeting variations in gastrointestinal cancer cellularity, organisation, metabolism, and oxygenation with MRI and radiotherapy using artificial intelligence to enable individualised cancer treatment.
Secondary ID [1] 310785 0
Nil
Universal Trial Number (UTN)
Trial acronym
VIBRANT
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Colon cancer 331755 0
Rectal cancer 331756 0
Pancreatic cancer 331757 0
Condition category
Condition code
Cancer 328504 328504 0 0
Bowel - Back passage (rectum) or large bowel (colon)
Cancer 328505 328505 0 0
Pancreatic

Intervention/exposure
Study type
Interventional
Description of intervention(s) / exposure
This project will focus on gastrointestinal cancers (pancreatic and colorectal) that often have poorer outcomes after standard treatment. This project will use ultra-high field MRI to characterise tumour heterogeneity at microscopic resolutions and correlate MRI findings with ‘ground-truth’ histopathology. Deep learning (artificial intelligence) will be used for the first time in cancer to characterise cancer heterogeneity with MRI, and translate these findings to clinical MRI to enhance the quality and resolution of clinical MRI images. MRI whole tumour ‘virtual biopsy’ will provide previously unobtainable information on tumour microenvironment and biologic behaviour (e.g. oxygenation, metabolism, cellularity and organisation). This will enable personalisation of patient treatment and allow MRI-Linacs and other therapies to target changes in cancer biology, leading to better cancer control and less side-effects.

Deep learning models that will be explored include (i) deep learning super-resolution - a method of obtaining a higher resolution image from clinical MR image (ii) convolutional neural network (iii) generative adversarial network. Radiomics analysis (computer-aided high-throughput analysis of medical images that can extract hundreds to thousands of quantitative or textural imaging parameters) may also be used in the analysis of ex vivo and clinical MRIs. The ability of developed deep learning models to produce a ‘high-resolution’ clinical MRI (i.e. whole tumour MRI ‘virtual biopsy’) will be tested on patient clinical MRI images.

Patient involvement in this study includes an additional MRI performed anytime between diagnosis and surgery, and consent to tissue donation/access to their surgical specimen for MRI scanning. The MRI scan will be performed by suitably qualified allied health professionals, using a scan protocol specifically developed for the study to ensure adherence to the study intervention. The duration of the scan is approximately 30-45mins, and may include the administration of a contrast agent.

MRI acquisition:
Standard clinical multiparametric MRI examination including anatomical (T2-weighted) and Diffusion Weighted Imaging (DWI) will be acquired. Research functional sequences will also be acquired. Clinical imaging of gastrointestinal tumours is affected by respiratory and bowel motion, and the sequences will be optimised to either short acquisition times, or respiratory gating within the time period of a standard clinical examination. A range of in vivo DWI datasets with different b-values and directions will be acquired. Various models will be applied (e.g. non-gaussian) to gauge heterogeneity in tumour cellularity, vasculature and organisation. Dynamic Contrast Enhanced (DCE) MRI will also be acquired with an optimised temporal and spatial resolution and perfusional models constructed (e.g. Tofts). Other sequences will be acquired to assess tumour heterogeneity: these include advanced diffusion sequences e.g. Diffusion Tensor Imaging (DTI), Intravoxel Incoherent Motion, (IVIM) to assess cellularity, Chemical Exchange Saturation Transfer (CEST) to assess metabolism and Blood Oxygenation Level Dependent (BOLD) MRI and Mapping of Oxygen By Imaging Lipids relaxation Enhancement (MOBILE) will be acquired to assess tumour oxygenation. Works-in-progress sequences may be used. These clinical images will be compiled into an atlas for clinical validation in this project and future studies.

Patients will undergo standard treatment as recommended by their treating team. Patients will undergo standard surgery +/- radiotherapy +/- chemotherapy +/- molecular targeted therapies +/- immunotherapy. There is no change to patient treatment on this study.
Intervention code [1] 327219 0
Diagnosis / Prognosis
Comparator / control treatment
No control group
Control group
Uncontrolled

Outcomes
Primary outcome [1] 336351 0
Characterise tumour heterogeneity with MRI i.e. cancer cellularity, vasculature, oxygenation, metabolism and proteins (composite primary outcome)
Timepoint [1] 336351 0
The cancer tissue being scanned is taken at the time of surgery
Primary outcome [2] 336352 0
Image quality of clinical MRI's produced by deep-learning algorithm based on ultra-high field ex vivo MRI
Timepoint [2] 336352 0
The cancer tissue being scanned is taken at the time of surgery
Primary outcome [3] 336353 0
Clinical validation of the deep learning models on patient MRI
Timepoint [3] 336353 0
Clinical MRI's will be undertaken prior to surgery. Cumulative participant clinical MRI and data will be collected from the outset of the project to allow for clinical validation of project results
Secondary outcome [1] 427975 0
Nil
Timepoint [1] 427975 0
Nil

Eligibility
Key inclusion criteria
1. Any of the following diagnoses:
a. Primary bowel cancer (colon or rectal)
b. Primary pancreatic cancer
2. Adult >= 18 years
3. Treatment consisting of surgery +/- (chemo)radiotherapy +/- other systemic therapies
4. Patient consent to study
Minimum age
18 Years
Maximum age
No limit
Sex
Both males and females
Can healthy volunteers participate?
No
Key exclusion criteria
1. Contra-indication to MRI
a. Implanted magnetic metal e.g. intraocular metal
b. Pacemaker / implantable defibrillator
c. Extreme claustrophobia

Study design
Purpose of the study
Diagnosis
Allocation to intervention
Non-randomised trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
Masking / blinding
Who is / are masked / blinded?



Intervention assignment
Other design features
Phase
Not Applicable
Type of endpoint/s
Statistical methods / analysis

Recruitment
Recruitment status
Not yet recruiting
Date of first participant enrolment
Anticipated
1/01/2025
Actual
Date of last participant enrolment
Anticipated
1/01/2027
Actual
Date of last data collection
Anticipated
1/02/2027
Actual
Sample size
Target
75
Accrual to date
Final
Recruitment in Australia
Recruitment state(s)
NSW
Recruitment hospital [1] 25744 0
Liverpool Hospital - Liverpool
Recruitment hospital [2] 25745 0
Campbelltown Hospital - Campbelltown
Recruitment hospital [3] 25746 0
Prince of Wales Hospital - Randwick
Recruitment postcode(s) [1] 41568 0
2170 - Liverpool
Recruitment postcode(s) [2] 41569 0
2560 - Campbelltown
Recruitment postcode(s) [3] 41570 0
2031 - Randwick

Funding & Sponsors
Funding source category [1] 315017 0
Charities/Societies/Foundations
Name [1] 315017 0
Cancer Council NSW
Country [1] 315017 0
Australia
Funding source category [2] 315052 0
Other Collaborative groups
Name [2] 315052 0
SPHERE
Country [2] 315052 0
Australia
Primary sponsor type
Government body
Name
South Western Sydney Local Health District
Address
Administration Building, Eastern Campus, Liverpool Hospital Locked Bag 7279, Liverpool BC 1871
Country
Australia
Secondary sponsor category [1] 317075 0
None
Name [1] 317075 0
Address [1] 317075 0
Country [1] 317075 0

Ethics approval
Ethics application status
Approved
Ethics committee name [1] 313986 0
South Western Sydney Local Health District Human Research Ethics Committee
Ethics committee address [1] 313986 0
Research Directorate, Locked Bag 7279, Eastern Campus Liverpool BC, NSW 1871
Ethics committee country [1] 313986 0
Australia
Date submitted for ethics approval [1] 313986 0
20/12/2022
Approval date [1] 313986 0
20/01/2023
Ethics approval number [1] 313986 0

Summary
Brief summary
The aim of this project is to compare a variety of magnetic resonance imaging (MRI) scans with several key cancer biological characteristics after the cancer has been removed and inspected microscopically. This project will focus on bowel (colon and rectum) and pancreatic cancers.

Who is it for?
You may be eligible for this study if you are aged 18 years or older, you have been diagnosed with bowel cancer (colon or rectal) or pancreatic cancer and you will be undergoing treatment consisting of surgery with or without (chemo)radiotherapy and/or any other systemic therapies.

Study details
All participants who choose to enrol in this study will be asked to undergo an MRI scan before surgery. The MRI scan uses a strong magnet to take images of cancer and surrounding tissues. MRI can assess the variations in biology, such as oxygenation and metabolism, within the cancer and tissues. The scan involves lying on a bed that moves into a tunnel for images to be taken. The MRI scan will take approximately 30 to 45 minutes. Participants may be given an injection of contrast (dye) to improve the quality of the scans. After surgery to remove the cancer, the tumour sample will be processed and analysed in a more detailed manner to help us merge the images and pathology findings. This involves (i) putting the entire removed cancer surgical specimen through a further MRI scan and/or (ii) taking a small tissue sample from the tumour that has been removed by surgery. This tissue sample will only be collected if not required by the Pathologist for diagnostic purposes. The MRI images will then be aligned and correlated with the pathology. None of these procedures completed after surgery will require any additional contribution by participants.

It is hoped this research will demonstrate the ability of artificial intelligence to combine high resolution MR images together with additional biological data (from pathology assessments) to accurately assess the characteristics of individual participants' cancer cells. If the technology is able to provide accurate information for an individual's specific cancer cell, this may allow doctors to better prescribe personalised treatments for each cancer patient which could improve their treatment success.
Trial website
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 130030 0
Dr Trang Pham
Address 130030 0
SWSLHD Radiation Oncology, Locked Bag 7103, Liverpool BC, 1871 NSW
Country 130030 0
Australia
Phone 130030 0
+61 2 87389805
Fax 130030 0
Email 130030 0
[email protected]
Contact person for public queries
Name 130031 0
Radiation Oncology Clinical Trials
Address 130031 0
SWSLHD Radiation Oncology, Locked Bag 7103, Liverpool BC, 1871 NSW
Country 130031 0
Australia
Phone 130031 0
+61 429094402
Fax 130031 0
Email 130031 0
[email protected]
Contact person for scientific queries
Name 130032 0
Trang Pham
Address 130032 0
SWSLHD Radiation Oncology, Locked Bag 7103, Liverpool BC, 1871 NSW
Country 130032 0
Australia
Phone 130032 0
+61 2 87389805
Fax 130032 0
Email 130032 0
[email protected]

Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
No
No/undecided IPD sharing reason/comment
Data will be analysed to seek correlations and trends. Results will be presented as a whole


What supporting documents are/will be available?

No Supporting Document Provided



Results publications and other study-related documents

Documents added manually
No documents have been uploaded by study researchers.

Documents added automatically
No additional documents have been identified.