Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence

Treatment Monitoring in Multiple Sclerosis - Efficacy and Safety

Magnetic resonance imaging is the most sensitive method for detecting inflammatory activity in multiple sclerosis, particularly in the brain where it reveals subclinical inflammation. Established MRI markers include contrast-enhancing lesions and active T2 lesions. Recent promising markers like slowly expanding lesions and phase rim lesions are being explored for monitoring chronic inflammation, but require further validation for clinical use. Volumetric and quantitative MRI techniques are currently limited to clinical trials and are not yet recommended for routine clinical use. Additionally, MRI is crucial for detecting complications from disease-modifying treatments and for implementing MRI-based pharmacovigilance strategies, such as in patients treated with natalizumab.

Artificial Intelligence and Multiple Sclerosis

In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.

Automatic lesion detection at Multiple Sclerosis patients - Comparison of 2D- and 3D-FLAIR-datasets

BACKGROUND

Multiple Sclerosis (MS) is a common autoimmune inflammatory disease of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) allows a sensitive assessment of the CNS and is established for diagnostic, prognostic and (therapy-) monitoring purposes. Especially lesion counting in T2- or Fluid Attenuated Inversion Recovery (FLAIR)-weighted images plays a decisive role in clinical routine. Software-packages allowing an automatic evaluation of image data are increasingly established aiming a faster and improved workflow. These programs allow e.g. the counting, spatial attribution and volumetry of MS-lesions in FLAIR-weighted images. Research has shown that 3D-FLAIR-sequences are superior to 2D-FLAIR-sequences in visual evaluation of lesion burden in MS. An influence on the automatic analysis is expectable but not yet systematically studied. This work will therefore investigate the influence of 2D- and 3D datasets on the results of an automatic assessment.

MATERIAL AND METHODS

In this prospective study, 80 Multiple Sclerosis patients underwent a clinically indicated routine MRI examination. The clinical routine protocol already including a 3D-FLAIR sequence was adapted by an additional 2D-FLAIR sequence also conform to the 2021 MAGNIMS-CMSCNAIMS consensus recommendations. To obtain a quantitative analysis for assessment of amount, dissemination and volume of the lesions, the acquired MR images were post-processed using the CE-certified Software mdbrain (mediaire, Berlin, Germany). The resulting data were statistically analysed using the paired t-test for normally distributed data and the Wilcoxon-signed-rank-test for not normally distributed data respectively. Demographic data and data such as the subtype, duration, severity and therapy of the disease were collected, pseudonymized and evaluated.

RESULTS

There is a significant difference concerning the total number and lesion volume with more lesions being detected (2D: 29.7, +/- 20.22 sd; 3D: 40.1 +/- 31.67 sd; p < 0.0001) but lower total volume (2D: 6.24 +/- 6.11 sd; 3D: 5.39 +/- 6.37 sd; p < 0.0001) when using the 3D- sequence. Especially significantly more small lesions in the unspecific white matter and infratentorial region were detected by using the 3D-FLAIR sequence (p < 0.0001) compared to the 2D-FLAIR image. Main reason for the lower total volume in the 3D-FLAIR sequence was the calculated volume for periventricular lesions which was significantly beneath the calculated volume from the 2D-FLAIR sequence (p < 0.0001).

CONCLUSION

Automatic lesion counting and volumetry is feasible with both 2D- and 3D-weightend FLAIR images. Still, it leads to partly significant differences even between two sequences that both are conform to the 2021 MAGNIMS-CMSCNAIMS consensus recommendations. This study contributes valuable insights into the impact of using different input data from the same patient for automated MS lesion evaluation.

The new era of artificial intelligence in neuroradiology: current research and promising tools

Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.

Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods

Over the past decades, morphometric analysis of brain MRI has contributed substantially to the understanding of healthy brain structure, development and aging as well as to improved characterisation of disease related pathologies. Certified commercial tools based on normative modeling of these metrics are meanwhile available for diagnostic purposes, but they are cost intensive and their clinical evaluation is still in its infancy. Here we have compared the performance of "ScanOMetrics", an open-source research-level tool for detection of statistical anomalies in individual MRI scans, depending on whether it is operated on the output of FreeSurfer or of the deep learning based brain morphometry tool DL + DiReCT. When applied to the public OASIS3 dataset, containing patients with Alzheimer's disease (AD) and healthy controls (HC), cortical thickness anomalies in patient scans were mainly detected in regions that are known as predilection areas of cortical atrophy in AD, regardless of the software used for extraction of the metrics. By contrast, anomaly detections in HCs were up to twenty-fold reduced and spatially unspecific using both DL + DiReCT and FreeSurfer. Progression of the atrophy pattern with clinical dementia rating (CDR) was clearly observable with both methods. DL + DiReCT provided results in less than 25 min, more than 15 times faster than FreeSurfer. This difference in computation time might be relevant when considering application of this or similar methodology as diagnostic decision support for neuroradiologists.

Changes in MRI Workflow of Multiple Sclerosis after Introduction of an AI-Software: A Qualitative Study

The purpose of this study was to explore the effects of the integration of machine learning into daily radiological diagnostics, using the example of the machine learning software mdbrain® (Mediaire GmbH, Germany) in the diagnostic MRI workflow of patients with multiple sclerosis at the University Medicine Greifswald. The data were assessed through expert interviews, a comparison of analysis times with and without the machine learning software, as well as a process analysis of MRI workflows. Our results indicate a reduction in the screen-reading workload, improved decision-making regarding contrast administration, an optimized workflow, reduced examination times, and facilitated report communication with colleagues and patients. Our results call for a broader and quantitative analysis.

Clinical Evaluation of a Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis

OBJECTIVE

To evaluate the influence of quantitative reports (QReports) on the radiological assessment of hippocampal sclerosis (HS) from MRI of patients with epilepsy in a setting mimicking clinical reality.

METHODS

The study included 40 patients with epilepsy, among them 20 with structural abnormalities in the mesial temporal lobe (13 with HS). Six raters blinded to the diagnosis assessed the 3T MRI in two rounds, first using MRI only and later with both MRI and the QReport. Results were evaluated using inter-rater agreement (Fleiss' kappa [Formula: see text]) and comparison with a consensus of two radiological experts derived from clinical and imaging data, including 7T MRI.

RESULTS

For the primary outcome, diagnosis of HS, the mean accuracy of the raters improved from 77.5% with MRI only to 86.3% with the additional QReport (effect size [Formula: see text]). Inter-rater agreement increased from [Formula: see text] to [Formula: see text]. Five of the six raters reached higher accuracies, and all reported higher confidence when using the QReports.

CONCLUSION

In this pre-use clinical evaluation study, we demonstrated clinical feasibility and usefulness as well as the potential impact of a previously suggested imaging biomarker for radiological assessment of HS.

Impact of automatic tools for detecting new lesions on therapeutic strategies offered to patients with MS by neurologists

BACKGROUND

Automatic tools for detecting new lesions in patients with MS between two MRI scans are now available to clinicians. They have been assessed from the radiologist's point of view, but their impact on the therapeutic strategies that neurologists offer their patients has not yet been documented.

OBJECTIVES

To compare neurologist's decisions according to whether a lesion detection support system had been used and describe variability between neurologists on decision-making for the same clinical cases.

METHODS

We submitted 28 clinical cases associated with pairs of MRI images and radiological reports (produced by the same radiologist without vs. with the help of a system to detect new lesions) to 10 neurologists who regularly follow patients with MS. They examined each clinical case twice (without vs. with support system) in two sessions several weeks apart, and their patient management decisions were recorded.

RESULTS

There was considerable variability between neurologists on decision-making (both with and without support system). When the support system had been used, neurologists more often made changes to patient management (75 % vs. 68 % of cases, p = 0.01) and spent significantly less time analyzing the clinical cases (249 s vs. 216 s, p == 3.10-4).

CONCLUSION

The use of a lesion detection support system has an impact not only on radiologists' reports, but also on neurologists' subsequent decision-making. This observation constitutes another strong argument for promoting the wider use of such systems in clinical routine. However, despite their use, there is still considerable variability in decision-making across neurologists, which should encourage us to refine the guidelines.

A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis

Modern management of MS targets No Evidence of Disease Activity (NEDA): no clinical relapses, no magnetic resonance imaging (MRI) disease activity and no disability worsening. While MRI is the principal tool available to neurologists for monitoring clinically silent MS disease activity and, where appropriate, escalating treatment, standard radiology reports are qualitative and may be insensitive to the development of new or enlarging lesions. Existing quantitative neuroimaging tools lack adequate clinical validation. In 397 multi-center MRI scan pairs acquired in routine practice, we demonstrate superior case-level sensitivity of a clinically integrated AI-based tool over standard radiology reports (93.3% vs 58.3%), relative to a consensus ground truth, with minimal loss of specificity. We also demonstrate equivalence of the AI-tool with a core clinical trial imaging lab for lesion activity and quantitative brain volumetric measures, including percentage brain volume loss (PBVC), an accepted biomarker of neurodegeneration in MS (mean PBVC -0.32% vs -0.36%, respectively), whereas even severe atrophy (>0.8% loss) was not appreciated in radiology reports. Finally, the AI-tool additionally embeds a clinically meaningful, experiential comparator that returns a relevant MS patient centile for lesion burden, revealing, in our cohort, inconsistencies in qualitative descriptors used in radiology reports. AI-based image quantitation enhances the accuracy of, and value-adds to, qualitative radiology reporting. Scaled deployment of these tools will open a path to precision management for patients with MS.

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges

INTRODUCTION

Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner. In this review, we provide an overview of the current state and challenges that clinicians, administrative personnel and policy makers need to be aware of and mitigate for the technology to reach its full potential.

AREAS COVERED

The article provides a brief targeted overview of AI, a high-level review of the current state and future potential AI applications in diagnostic radiology and to a lesser extent digital pathology, focusing on oncologic applications. This is followed by a discussion of emerging approaches, including multimodal models. The article concludes with a discussion of technical, regulatory challenges and infrastructure needs for AI to realize its full potential.

EXPERT OPINION

There is a large volume of promising research, and steadily increasing commercially available tools using AI. For the most advanced and promising precision diagnostic applications of AI to be used clinically, robust and comprehensive quality monitoring systems and informatics platforms will likely be required.

Super-Resolution Reconstruction of Multi-Slice T2-W FLAIR MRI Improves Multiple Sclerosis Lesion Segmentation

Due to acquisition time constraints, T2-w FLAIR MRI of Multiple Sclerosis (MS) patients is often acquired with multi-slice 2D protocols with a low through-plane resolution rather than with high-resolution 3D protocols. Automated lesion segmentation on such low-resolution (LR) images, however, performs poorly and leads to inaccurate lesion volume estimates. Super-resolution reconstruction (SRR) methods can then be used to obtain a high-resolution (HR) image from multiple LR images to serve as input for lesion segmentation. In this work, we evaluate the effect on MS lesion segmentation of three SRR approaches: one based on interpolation, a state-of-the-art self-supervised CNN-based strategy, and a recently proposed model-based SRR method. These SRR strategies were applied to LR acquisitions simulated from 3D T2-w FLAIR MRI of MS patients. Each SRR method was evaluated in terms of image reconstruction quality and subsequent lesion segmentation performance. When compared to segmentation on LR images, the three considered SRR strategies demonstrate improved lesion segmentation. Furthermore, in some scenarios, SRR achieves a similar segmentation performance compared to segmentation of HR images.Clinical relevance- This study demonstrates the positive impact of super-resolution reconstruction from T2-w FLAIR multi-slice MRI acquisitions on segmentation performance of MS lesions.