An Initiative to Reduce Unnecessary Gadolinium-Based Contrast in Multiple Sclerosis Patients

MRI Gadolinium-Based Contrast Media: Meeting Radiological, Clinical, and Environmental Needs

Gadolinium-based contrast agents (GBCAs) are routinely used in magnetic resonance imaging (MRI). They are essential for choosing the most appropriate medical or surgical strategy for patients with serious pathologies, particularly in oncologic, inflammatory, and cardiovascular diseases. However, GBCAs have been associated with an increased risk of nephrogenic systemic fibrosis in patients with renal failure, as well as the possibility of deposition in the brain, bones, and other organs, even in patients with normal renal function. Research is underway to reduce the quantity of gadolinium injected, without compromising image quality and diagnosis. The next generation of GBCAs will enable a reduction in the gadolinium dose administered. Gadopiclenol is the first of this new generation of GBCAs, with high relaxivity, thus having the potential to reduce the gadolinium dose while maintaining good in vivo stability due to its macrocyclic structure. High-stability and high-relaxivity GBCAs will be one of the solutions for reducing the dose of gadolinium to be administered in clinical practice, while the development of new technologies, including optimization of MRI acquisitions, new contrast mechanisms, and artificial intelligence may help reduce the need for GBCAs. Future solutions may involve a combination of next-generation GBCAs and image-processing techniques to optimize diagnosis and treatment planning while minimizing exposure to gadolinium. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

Choroid plexus volume in multiple sclerosis can be estimated on structural MRI avoiding contrast injection

We compared choroid plexus (ChP) manual segmentation on non-contrast-enhanced (non-CE) sequences and reference standard CE T1- weighted (T1w) sequences in 61 multiple sclerosis patients prospectively included. ChP was separately segmented on T1w, T2-weighted (T2w) fluid-attenuated inversion-recovery (FLAIR), and CE-T1w sequences. Inter-rater variability assessed on 10 subjects showed high reproducibility between sequences measured by intraclass correlation coefficient (T1w 0.93, FLAIR 0.93, CE-T1w 0.99). CE-T1w showed higher signal-to-noise ratio and contrast-to-noise ratio (CE-T1w 23.77 and 18.49, T1w 13.73 and 7.44, FLAIR 13.09 and 10.77, respectively). Manual segmentation of ChP resulted 3.073 ± 0.563 mL (mean ± standard deviation) on T1w, 3.787 ± 0.679 mL on FLAIR, and 2.984 ± 0.506 mL on CE-T1w images, with an error of 28.02 ± 19.02% for FLAIR and 3.52 ± 12.61% for T1w. FLAIR overestimated ChP volume compared to CE-T1w (p < 0.001). The Dice similarity coefficient of CE-T1w versus T1w and FLAIR was 0.67 ± 0.05 and 0.68 ± 0.05, respectively. Spatial error distribution per slice was calculated after nonlinear coregistration to the standard MNI152 space and showed a heterogeneous profile along the ChP especially near the fornix and the hippocampus. Quantitative analyses suggest T1w as a surrogate of CE-T1w to estimate ChP volume.Relevance statement To estimate the ChP volume, CE-T1w can be replaced by non-CE T1w sequences because the error is acceptable, while FLAIR overestimates the ChP volume. This encourages the development of automatic tools for ChP segmentation, also improving the understanding of the role of the ChP volume in multiple sclerosis, promoting longitudinal studies.Key points • CE-T1w sequences are considered the reference standard for ChP manual segmentation.• FLAIR sequences showed a higher CNR than T1w sequences but overestimated the ChP volume.• Non-CE T1w sequences can be a surrogate of CE-T1w sequences for manual segmentation of ChP.

[Use of Gadolinium in Follow-Up MRI of Multiple Sclerosis Patients: Current Recommendations]

Multiple sclerosis is the most frequent demyelinating disease of the central nervous system and is characterized by early onset and progressive disability. Magnetic resonance imaging, due to its high sensitivity and specificity in the detection of demyelinating lesions, is the most useful diagnostic test for this disease, with the administration of gadolinium-based contrast agents being an important contribution to imaging interpretation. Although contrast is essential for diagnostic purposes, its routine use in monitoring disease activity, response to treatment, and related complications is controversial. This article aims to collate current recommendations regarding the use of gadolinium in the imaging follow-up of multiple sclerosis and establish effective and safe guidelines for clinical practice. The literature review was conducted in PubMed, using the terms 'multiple sclerosis', 'magnetic resonance imaging' and 'gadolinium', or 'contrast media'. Articles published between January 2013 and January 2023 concerning the safety of gadolinium and the use of these contrast agents in follow-up scans of adult patients diagnosed with multiple sclerosis were selected. Although no biological or clinical consequences have been unequivocally attributed to the retention of gadolinium in the brain, which were mostly reported with linear agents, health authorities have been recommending the restriction of contrast to essential clinical circumstances. In multiple sclerosis, the detection of subclinical contrast-enhancing lesions with no corresponding new/ enlarging T2-WI lesions is rare and has a questionable impact on therapeutic decisions. On the other hand, gadolinium has a higher sensitivity in the differential diagnosis of relapses, in the detection of recent disease activity, before and after treatment initiation, and in patients with a large lesion burden or diffuse/confluent T2-WI lesions. Contrary to progressive multifocal leukoencephalopathy screening, monitoring of immune restitution inflammatory syndrome also benefits from the administration of gadolinium. It is feasible and safe to exclude gadolinium-based contrast agents from routine follow-up scans of multiple sclerosis, despite their additional contribution in specific clinical circumstances that should be acknowledged by the neurologist and neuroradiologist.

A Beginner's Guide to Arterial Spin Labeling (ASL) Image Processing

Arterial spin labeling (ASL) is a non-invasive and cost-effective MRI technique for brain perfusion measurements. While it has developed into a robust technique for scientific and clinical use, its image processing can still be daunting. The 2019 Ann Arbor ISMRM ASL working group established that education is one of the main areas that can accelerate the use of ASL in research and clinical practice. Specifically, the post-acquisition processing of ASL images and their preparation for region-of-interest or voxel-wise statistical analyses is a topic that has not yet received much educational attention. This educational review is aimed at those with an interest in ASL image processing and analysis. We provide summaries of all typical ASL processing steps on both single-subject and group levels. The readers are assumed to have a basic understanding of cerebral perfusion (patho) physiology; a basic level of programming or image analysis is not required. Starting with an introduction of the physiology and MRI technique behind ASL, and how they interact with the image processing, we present an overview of processing pipelines and explain the specific ASL processing steps. Example video and image illustrations of ASL studies of different cases, as well as model calculations, help the reader develop an understanding of which processing steps to check for their own analyses. Some of the educational content can be extrapolated to the processing of other MRI data. We anticipate that this educational review will help accelerate the application of ASL MRI for clinical brain research.

Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions

Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system that affects nearly 1 million adults in the United States. Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis and treatment monitoring in MS patients. In particular, follow-up MRI with T2-FLAIR images of the brain, depicting white matter lesions, is the mainstay for monitoring disease activity and making treatment decisions. In this article, we present a computational approach that has been deployed and integrated into a real-world routine clinical workflow, focusing on two tasks: (a) detecting new disease activity in MS patients, and (b) determining the necessity for injecting Gadolinium Based Contract Agents (GBCAs). This computer-aided detection (CAD) software has been utilized for the former task on more than 19, 000 patients over the course of 10 years, while its added function of identifying patients who need GBCA injection, has been operative for the past 3 years, with > 85% sensitivity. The benefits of this approach are summarized in: (1) offering a reproducible and accurate clinical assessment of MS lesion patients, (2) reducing the adverse effects of GBCAs (and the deposition of GBCAs to the patient's brain) by identifying the patients who may benefit from injection, and (3) reducing healthcare costs, patients' discomfort, and caregivers' workload.

Economic impact of selective use of contrast for routine follow-up MRI of patients with multiple sclerosis

BACKGROUND AND PURPOSE

Imaging and autopsy studies show intracranial gadolinium deposition in patients who have undergone serial contrast-enhanced MRIs. This observation has raised concerns when using contrast administration in patients who receive frequent MRIs. To address this, we implemented a contrast-conditional protocol wherein gadolinium is administered only for multiple sclerosis (MS) patients with imaging evidence of new disease activity on precontrast imaging. In this study, we explore the economic impact of our new MRI protocol.

METHODS

We compared scanner time and Medicare reimbursement using our contrast-conditional methodology versus that of prior protocols where all patients received gadolinium.

RESULTS

For 422 patients over 4 months, the contrast-conditional protocol amounted to 60% decrease in contrast injection and savings of approximately 20% of MRI scanner time. If the extra scanner time was used for performing MS follow-up MRIs in additional patients, the contrast-conditional protocol would amount to net revenue loss of $21,707 (∼3.7%).

CONCLUSIONS

Implementation of a new protocol to limit contrast in MS follow-up MRIs led to a minimal decrease in revenue when controlled for scanner time utilized and is outweighed by other benefits, including substantial decreased gadolinium administration, increased patient comfort, and increased availability of scanner time, which depending on type of studies performed could result in additional financial benefit.

Diagnostic value of gadolinium contrast administration for spinal cord magnetic resonance imaging in multiple sclerosis patients and correlative markers of lesion enhancement

Background

Magnetic resonance imaging is essential for monitoring people with multiple sclerosis, but the diagnostic value of gadolinium contrast administration in spine magnetic resonance imaging is unclear.

Objective

To assess the diagnostic value of gadolinium contrast administration in spine magnetic resonance imaging follow-up examinations and identify imaging markers correlating with lesion enhancement.

Methods

A total of 65 multiple sclerosis patients with at least 2 spinal magnetic resonance imaging follow-up examinations were included. Spine magnetic resonance imaging was performed at 3 Tesla with a standardized protocol (sagittal and axial T2-weighted turbo spin echo and T1-weighted post-contrast sequences). T2 lesion load and enhancing lesions were assessed by two independent neuroradiologists for lesion size, localization, and T2 signal ratio (T2 signal_lesion/T2 signal_{normal appearing spinal cord}).

Results

A total of 68 new spinal T2 lesions and 20 new contrast-enhancing lesions developed during follow-up. All enhancing lesions had a discernable correlate as a new T2 lesion. Lesion enhancement correlated with a higher T2 signal ratio compared to non-enhancing lesions (T2 signal ratio: 2.0 ± 0.4 vs. 1.4 ± 0.2, ****p < 0.001). Receiver operating characteristics analysis showed an optimal cutoff value of signal ratio 1.78 to predict lesion enhancement (82% sensitivity and 97% specificity).

Conclusion

Gadolinium contrast administration is dispensable in follow-up spine magnetic resonance imaging if no new T2 lesions are present. Probability of enhancement correlates with the T2 signal ratio.

The role of contrast-enhanced and non-contrast-enhanced MRI in the follow-up of multiple sclerosis

BACKGROUND

Magnetic resonance imaging (MRI) is widely used in patients with multiple sclerosis (MS) for different indications. However, frequent administration of gadolinium in these patients can have some potential complications. So, a more limited approach reducing the use of gadolinium should be considered.

PURPOSE

To evaluate the additional benefits of contrast-enhanced MRI over non-contrast-enhanced MRI in routine follow-up of patients with MS.

MATERIAL AND METHODS

This is a retrospective cohort study including patients with MS who underwent both contrast-enhanced and non-contrast-enhanced MRI for two time-points with an interval of at least six months. Non-contrast-enhanced images were compared for each patient and interpreted as non-progressive or progressive disease. Then, rate and type of enhancing lesions were analyzed and compared between the groups. All images were reviewed and compared visually by two radiologists.

RESULTS

A total of 462 patients (392 women; mean age = 36 years) were included. Of these patients, 352 were in the non-progressive group and 112 were in progressive group. Comparison of baseline and follow-up contrast-enhanced MRIs revealed that 13 (3.7%) patients in the non-progressive group and 58 (51.8%) patients in progressive group developed enhancing lesions (P < 0.001). All 58 patients in the progressive group developed new enhancing lesions, whereas all those in the non-progressive group revealed persistent or reactivated enhancing lesions without evidence of new lesions.

CONCLUSION

According to the very low incidence rate of new enhancing lesions in patients with non-progressive disease on follow-up non-contrast-enhanced MRI, routine administration of contrast in follow-up studies is not suggested.

Magnetic resonance imaging diagnosis of subependymal giant cell astrocytomas in follow-up of children with tuberous sclerosis complex: should we always use contrast enhancement?

BACKGROUND

Subependymal giant cell astrocytomas (SEGAs) arise in 10-26% of tuberous sclerosis complex (TSC) patients. SEGAs cause obstructive hydrocephalus and increase morbi-mortality. It is recommended that TSC patients be followed with contrast enhanced magnetic resonance imaging (CE-MRI), but repetitive use of gadolinium-based contrast-agents (GBCAs) may cause organ deposits.

OBJECTIVE

To compare the diagnostic performances of non-CE- and CE-MRI to differentiate SEGAs from subependymal nodules in TSC patients during follow-up.

MATERIALS AND METHODS

Thirty-five TSC patients (median age: 2.4 years) were enrolled in this retrospective single-center study from September 2007 to January 2019. Inclusion criteria were a certain diagnosis of TSC and at least three follow-up brain MRIs with GBCA injection. Two consecutive MRI scans per patient were selected and anonymized. Three radiologists performed a blinded review of non-enhanced and enhanced MRI sequences during different sessions. The diagnostic performances were compared (sensitivity, specificity, positive/negative predictive values, accuracy, inter/intra-observer agreements).

RESULTS

The accuracies for detecting SEGAs were good and similar between the non-enhanced and enhanced MRI sequences. The sensitivity and specificity of non-CE-MRI to diagnose SEGA ranged from 75% to 100% and from 94% to 100%, respectively. The differences in numbers of false-positive and false-negative patients between non-CE- and CE-MRI never exceeded one case. Nodules size >10 mm, location near the Monro foramen, hydrocephalus and modifications between two consecutive MRI scans were significantly associated with the diagnosis of SEGA for the three readers (all P-values <0.05). Inter- and intra-observer agreements were also excellent for non-enhanced and enhanced MRI sequences (kappa=0.85-1 and 0.81-0.93, respectively).

CONCLUSION

The performances of non-enhanced and enhanced MRI sequences are comparable for detecting SEGAs, questioning the need for systematic GBCA injections for TSC patients.

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence

The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter- and intra-rater variability. Automated methods for lesion quantification, if accurate enough, hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separate radiologically progressive from radiologically stable patients, despite this being a pressing clinical use-case. In this paper, we explore the ability of a deep learning segmentation classifier to separate stable from progressive patients by lesion volume and lesion count, and find that neither measure provides a good separation. Instead, we propose a method for identifying lesion changes of high certainty, and establish on an internal dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable time-points with a very high level of discrimination (AUC = 0.999), while changes in lesion volume are much less able to perform this separation (AUC = 0.71). Validation of the method on two external datasets confirms that the method is able to generalize beyond the setting in which it was trained, achieving an accuracies of 75 % and 85 % in separating stable and progressive time-points.