Lesion location across diagnostic regions in multiple sclerosis

Pongratz, Viola; Bussas, Matthias; Schmidt, Paul; Grahl, Sophia; Gasperi, Christiane; Husseini, Malek El; Harabacz, Laura; Pineker, Viktor; Sepp, Dominik; Grundl, Lioba; Wiestler, Benedikt; Kirschke, Jan S.; Zimmer, Claus; Berthele, Achim; Hemmer, Bernhard; Mühlau, Mark

doi:10.1016/j.nicl.2022.103311

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…In our study, we also found significantly higher T2LV and T1LV in MS than in SDCNS patients. These results underscore focal white matter destruction as a process more pronounced in MS than in SDCNS patients beginning from the earliest phases of the disease, which is concordant with the understanding of CNS pathology in MS [5,14,58]. Additionally, this observation seems to support a less clear association of CNS symptoms with focal brain pathology in systemic diseases.…”

Section: Discussionsupporting

confidence: 79%

Differences in Brain Atrophy Pattern between People with Multiple Sclerosis and Systemic Diseases with Central Nervous System Involvement Based on Two-Dimensional Linear Measures

Siger,

Wydra,

Wildner

et al. 2024

JCM

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Conventional brain magnetic resonance imaging (MRI) in systemic diseases with central nervous system involvement (SDCNS) may imitate MRI findings of multiple sclerosis (MS). In order to better describe the MRI characteristics of these conditions, in our study we assessed brain volume parameters in MS (n = 58) and SDCNS (n = 41) patients using two-dimensional linear measurements (2DLMs): bicaudate ratio (BCR), corpus callosum index (CCI) and width of third ventricle (W3V). In SDCNS patients, all 2DLMs were affected by age (CCI p = 0.005, BCR p < 0.001, W3V p < 0.001, respectively), whereas in MS patients only BCR and W3V were (p = 0.001 and p = 0.015, respectively). Contrary to SDCNS, in the MS cohort BCR and W3V were associated with T1 lesion volume (T1LV) (p = 0.020, p = 0.009, respectively) and T2 lesion volume (T2LV) (p = 0.015, p = 0.009, respectively). CCI was associated with T1LV in the MS cohort only (p = 0.015). Moreover, BCR was significantly higher in the SDCNS group (p = 0.01) and CCI was significantly lower in MS patients (p = 0.01). The best predictive model to distinguish MS and SDCNS encompassed gender, BCR and T2LV as the explanatory variables (sensitivity 0.91; specificity 0.68; AUC 0.86). Implementation of 2DLMs in the brain MRI analysis of MS and SDCNS patients allowed for the identification of diverse patterns of local brain atrophy in these clinical conditions.

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Section: Discussionsupporting

confidence: 79%

Differences in Brain Atrophy Pattern between People with Multiple Sclerosis and Systemic Diseases with Central Nervous System Involvement Based on Two-Dimensional Linear Measures

Siger,

Wydra,

Wildner

et al. 2024

JCM

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“…Across 5 adult marmosets immunized with hMOG/IFA emulsion, WM areas, including the optic tract (opt), visual projections, and commissural fibers that connect the brain hemispheres appear most vulnerable to inflammatory demyelination ( Fig1B ). The anatomical structures of these demyelination hotspots are largely periventricular, which phenocopies the prevalence of periventricular lesions found in MS (24,25). Lesions of marmoset EAE show Gd enhancement when the blood-brain barrier (BBB) is open (26,27), the central vein sign in developing lesions (28), and iron accumulation at the lesion (29) recapitulating these aspects of MS.…”

Section: Mainmentioning

confidence: 84%

A 4D transcriptomic map for the evolution of multiple sclerosis-like lesions in the marmoset brain

Lin,

Brake,

Donadieu

et al. 2023

Preprint

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Single-time-point histopathological studies on postmortem multiple sclerosis (MS) tissue fail to capture lesion evolution dynamics, posing challenges for therapy development targeting development and repair of focal inflammatory demyelination. To close this gap, we studied experimental autoimmune encephalitis (EAE) in the common marmoset, the most faithful animal model of these processes. Using MRI-informed RNA profiling, we analyzed ∼600,000 single-nucleus and ∼55,000 spatial transcriptomes, comparing them against EAE inoculation status, longitudinal radiological signals, and histopathological features. We categorized 5 groups of microenvironments pertinent to neural function, immune and glial responses, tissue destruction and repair, and regulatory network at brain borders. Exploring perilesional microenvironment diversity, we uncovered central roles of EAE-associated astrocytes, oligodendrocyte precursor cells, and ependyma in lesion formation and resolution. We pinpointed imaging and molecular features capturing the pathological trajectory of WM, offering potential for assessing treatment outcomes using marmoset as a platform.One sentence summaryA cross-modality study to identify the spatiotemporal-based diversity of primate brain cells during white matter inflammatory demyelination to inform lesion detection, stratification, and management in multiple sclerosis.

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“…In MS, lesion location within the brain may play an important role in identifying different disease patterns (Pongratz et al, 2023). In the LST-AI toolbox, a method is included which is able to classify lesions into four categories according to their location (PV, IT, JC, and SC).…”

Section: Discussionmentioning

confidence: 99%

LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation

Wiltgen,

McGinnis,

Schlaeger

et al. 2023

Preprint

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Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced a lesion segmentation tool, LST, engineered with a lesion growth algorithm (LST-LGA). While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. Here, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D-UNets.LST-AI specifically addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1w and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI additionally includes a lesion location annotation tool, labeling lesion location according to the 2017 McDonald criteria (periventricular, infratentorial, juxtacortical, subcortical). We conduct evaluations on 270 test cases —comprising both in-house (n=167) and publicly available data (n=103)—using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models.Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.5, outperforming LST-LGA, LST-LPA, SAMSEG, and the popular nnUNet framework, which all scored below 0.45. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63—surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions larger than 60mm3.Given its higher segmentation performance, we recommend that research groups currently using LST-LGA transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms.

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Lesion location across diagnostic regions in multiple sclerosis

Cited by 7 publications

References 41 publications

Differences in Brain Atrophy Pattern between People with Multiple Sclerosis and Systemic Diseases with Central Nervous System Involvement Based on Two-Dimensional Linear Measures

Differences in Brain Atrophy Pattern between People with Multiple Sclerosis and Systemic Diseases with Central Nervous System Involvement Based on Two-Dimensional Linear Measures

A 4D transcriptomic map for the evolution of multiple sclerosis-like lesions in the marmoset brain

LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation

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