Clinical trial NCT05754476: Multi-center Study of Artificial Intelligence Model for Gadolinium-based Contrast Agent Reduction in Brain MRI (MAGNET)
Official title: Multi-center and Prospective Cohort Study of Artificial Intelligence Model for Gadolinium-based Contrast Agent Reduction in Brain MRI (MAGNET)
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Brain Diseases
Lead sponsor: Beijing Tiantan Hospital
Sponsor class: OTHER
First posted: 2023-03-03
Enrollment: 3000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: To reflect the daily practices, this study includes all patients with suspected or known brain diseases requiring MRI exams with GBCAs at the beginning of the study.

Brief summary: MAGNET is a multi-center and prospective study to minimize Gadolinium-based Contrast Agent (GBCA) combining novel artificial intelligence (AI) methods with pre-contrast images and/or low-dose images to synthesize virtual contrast-enhanced T1 (vir-T1c) images, based on a large clinical and MRI database and subsequently validated for its clinical value. MRI examinations for patients included T1-weighted images (T1WI) before and after contrast agent administration and at two dose levels: low-dose (10% or 25%) and full-dose (100%), T2-weighted images (T2WI), fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted imaging sequences (DWI) and the computed apparent diffusion coefficient (ADC), all either acquired three dimensional \[3D\] or two dimensional \[2D\]). The standard dose of intravenous gadolinium contrast agent was 0.1mmol/kg(body weight) by manual injection or automatic injection with a high-pressure syringe at a flow rate of 4mL/s.The sequence parameters used for the 3DT1WI scans must be consistent, and the standard for intravenous injection of gadolinium contrast agent is 0.1mmol/kg (body weight), administered either manually or automatically with a high-pressure syringe at a rate of 4mL/s.

Additionally, arterial spin labeling (ASL), amide-proton transfer chemical exchange saturation transfer (APT-CEST), susceptibility-weighted imaging (SWI), or quantitative susceptibility mapping (QSM) can be acquired at the same time if the conditions permit.

Detailed description: MRI with GBCA is an indispensable part of imaging exams for brain disease diagnosis. Generally, GBCA is safe, with a few mild side effects since GBCAs received FDA approval in 1989. There are numerous issues that challenge the current practice of widespread use of GBCA. GBCA can trigger nephrogenic systemic fibrosis(NSF) under particular circumstances, cause allergic reactions, may increase the risk of fetal death, and accumulate in the brain such as the dentate nucleus and globus pallidus. Efforts need to be made to reduce dose while still maintaining diagnostic capabilities. Artificial intelligence (AI) techniques have shown great potential in medical fields. Deep learning (DL), a branch of AI, has been applied to image segmentation, computer-aided diagnosis, and reduce GBCA dose.

This study intends to build a prospective brain MRI dataset including patients with suspected or known brain abnormalities to minimize the use of GBCA. Then train DL models to process pre-contrast images and/or low-dose T1 images to predict virtual contrast-enhanced T1 (vir-T1c) images, taking the full-dose images as the reference standard. Later quantitatively and qualitatively evaluating and comparing vir-T1c images from DL models about clinical diagnostic performance, focusing on lesion detection, diagnosis, and therapy, to explore a DL model universal, provide enhanced images faster and more convenient in clinical practice. To minimize the use of GBCA, we will:

1. Use novel artificial intelligence (AI) methods with pre-contrast images including conventional (T1WI, T2WI, FLAIR, DWI/ADC), new physiological MRI techniques (ASL, APT-CEST, SWI/QSM) by adding physiological information from perfusion as well as metabolic and susceptibility imaging, and/or low-dose images (10% or 25%) to synthesize vir-T1c images;
2. Quantify when (in which patients and at what follow-up times) GBCA can be omitted or minimized without influencing brain disease diagnosis and treatment evaluation for doctor raters and therefore patient prognosis.

This study does not limit manufacturers including 1.5T and 3.0T scanners， or kinds of GBCAs.

Eligibility criteria: Inclusion Criteria:

1. Patients with suspected or known brain diseases including tumors, vascular disorders, inflammatory diseases, neurodegenerative diseases and trauma, follow-up, routine brain, and others requiring MRI exams with GBCAs.
2. Informed written consent obtained from the patient, and/or patient's parent(s), and/or legal representative.

Exclusion Criteria:

1. Patients with contraindications to MR examination.
2. Patients with incomplete MRI data and obvious image artifacts.

Primary outcomes:
measure: quantitative metrics
timeFrame: after training and applying of the proposed deep learning model, an average of 1 year
measure: qualitative assessments
timeFrame: after training and applying of the proposed deep learning model, an average of 15 months
Secondary outcomes:
measure: clinical effects
timeFrame: after training and applying of the proposed deep learning model, an average of 18 month
Other outcomes:
Locations:
city: Beijing
country: China
facility: Beijing Tiantan Hospital
state: Beijing
Study officials:
affiliation: Study Principal Investigator
name: Yaou Liu, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Jayachandran Preetha C, Meredig H, Brugnara G, Mahmutoglu MA, Foltyn M, Isensee F, Kessler T, Pfluger I, Schell M, Neuberger U, Petersen J, Wick A, Heiland S, Debus J, Platten M, Idbaih A, Brandes AA, Winkler F, van den Bent MJ, Nabors B, Stupp R, Maier-Hein KH, Gorlia T, Tonn JC, Weller M, Wick W, Bendszus M, Vollmuth P. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. Lancet Digit Health. 2021 Dec;3(12):e784-e794. doi: 10.1016/S2589-7500(21)00205-3. Epub 2021 Oct 20.
citation: Luo H, Zhang T, Gong NJ, Tamir J, Venkata SP, Xu C, Duan Y, Zhou T, Zhou F, Zaharchuk G, Xue J, Liu Y. Deep learning-based methods may minimize GBCA dosage in brain MRI. Eur Radiol. 2021 Sep;31(9):6419-6428. doi: 10.1007/s00330-021-07848-3. Epub 2021 Mar 18.
citation: Gong E, Pauly JM, Wintermark M, Zaharchuk G. Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI. J Magn Reson Imaging. 2018 Aug;48(2):330-340. doi: 10.1002/jmri.25970. Epub 2018 Feb 13.