Misdiagnosis of multiple sclerosis: Impact of the 2017 McDonald criteria on clinical practice

Diagnostic Criteria for Multiple Sclerosis, Neuromyelitis Optica Spectrum Disorders, and Myelin Oligodendrocyte Glycoprotein-immunoglobulin G-associated Disease

The diagnostic workup of multiple sclerosis (MS) has evolved considerably. The 2017 revision of the McDonald criteria shows high sensitivity and accuracy in predicting clinically definite MS in patients with a typical clinically isolated syndrome and allows an earlier MS diagnosis. Neuromyelitis optica spectrum disorders (NMOSD) and myelin oligodendrocyte glycoprotein-immunoglobulin G-associated disease (MOGAD) are recognized as separate conditions from MS, with specific diagnostic criteria. New MR imaging markers may improve diagnostic specificity for these conditions, thus reducing the risk of misdiagnosis. This study summarizes the most recent updates regarding the application of MR imaging for the diagnosis of MS, NMOSD, and MOGAD.

Central Vein Sign, Cortical Lesions, and Paramagnetic Rim Lesions for the Diagnostic and Prognostic Workup of Multiple Sclerosis

BACKGROUND AND OBJECTIVES

The diagnosis of multiple sclerosis (MS) can be challenging in clinical practice because MS presentation can be atypical and mimicked by other diseases. We evaluated the diagnostic performance, alone or in combination, of the central vein sign (CVS), paramagnetic rim lesion (PRL), and cortical lesion (CL), as well as their association with clinical outcomes.

METHODS

In this multicenter observational study, we first conducted a cross-sectional analysis of the CVS (proportion of CVS-positive lesions or simplified determination of CVS in 3/6 lesions-Select3*/Select6*), PRL, and CL in MS and non-MS cases on 3T-MRI brain images, including 3D T2-FLAIR, T2*-echo-planar imaging magnitude and phase, double inversion recovery, and magnetization prepared rapid gradient echo image sequences. Then, we longitudinally analyzed the progression independent of relapse and MRI activity (PIRA) in MS cases over the 2 years after study entry. Receiver operating characteristic curves were used to test diagnostic performance and regression models to predict diagnosis and clinical outcomes.

RESULTS

The presence of ≥41% CVS-positive lesions/≥1 CL/≥1 PRL (optimal cutoffs) had 96%/90%/93% specificity, 97%/84%/60% sensitivity, and 0.99/0.90/0.77 area under the curve (AUC), respectively, to distinguish MS (n = 185) from non-MS (n = 100) cases. The Select3*/Select6* algorithms showed 93%/95% specificity, 97%/89% sensitivity, and 0.95/0.92 AUC. The combination of CVS, CL, and PRL improved the diagnostic performance, especially when Select3*/Select6* were used (93%/94% specificity, 98%/96% sensitivity, 0.99/0.98 AUC; p = 0.002/p < 0.001). In MS cases (n = 185), both CL and PRL were associated with higher MS disability and severity. Longitudinal analysis (n = 61) showed that MS cases with >4 PRL at baseline were more likely to experience PIRA at 2-year follow-up (odds ratio 17.0, 95% confidence interval: 2.1-138.5; p = 0.008), whereas no association was observed between other baseline MRI measures and PIRA, including the number of CL.

DISCUSSION

The combination of CVS, CL, and PRL can improve MS differential diagnosis. CL and PRL also correlated with clinical measures of poor prognosis, with PRL being a predictor of disability accrual independent of clinical/MRI activity.

Improving the classification of multiple sclerosis and cerebral small vessel disease with interpretable transfer attention neural network

As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple sclerosis (MS) is often confused with cerebral small vessel disease (cSVD), which is a regional pathological change in brain tissue with unknown pathogenesis. This is due to their similar clinical presentations and imaging manifestations. That misdiagnosis can significantly increase the occurrence of adverse events. Delayed or incorrect treatment is one of the most important causes of MS progression. Therefore, the development of a practical diagnostic imaging aid could significantly reduce the risk of misdiagnosis and improve patient prognosis. We propose an interpretable deep learning (DL) model that differentiates MS and cSVD using T2-weighted fluid-attenuated inversion recovery (FLAIR) images. Transfer learning (TL) was utilized to extract features from the ImageNet dataset. This pioneering model marks the first of its kind in neuroimaging, showing great potential in enhancing differential diagnostic capabilities within the field of neurological disorders. Our model extracts the texture features of the images and achieves more robust feature learning through two attention modules. The attention maps provided by the attention modules provide model interpretation to validate model learning and reveal more information to physicians. Finally, the proposed model is trained end-to-end using focal loss to reduce the influence of class imbalance. The model was validated using clinically diagnosed MS (n=112) and cSVD (n=321) patients from the Beijing Tiantan Hospital. The performance of the proposed model was better than that of two commonly used DL approaches, with a mean balanced accuracy of 86.06 % and a mean area under the receiver operating characteristic curve of 98.78 %. Moreover, the generated attention heat maps showed that the proposed model could focus on the lesion signatures in the image. The proposed model provides a practical diagnostic imaging aid for the use of routinely available imaging techniques such as magnetic resonance imaging to classify MS and cSVD by linking DL to human brain disease. We anticipate a substantial improvement in accurately distinguishing between various neurological conditions through this novel model.

Differential diagnosis between multiple sclerosis and leukodystrophies - A scoping review

Multiple Sclerosis (MS) is an autoimmune demyelinating disease of the central nervous system (CNS) characterized by damage to the myelin sheaths of oligodendrocytes. Currently, there is no specific biomarker to identify the disease; however, a diagnostic criterion has been established based on patient's clinical, laboratory, and imaging characteristics, which assists in identifying this condition. The primary method for diagnosing MS is the McDonald criteria, first described in 2001 and revised in the years 2005, 2012, and 2017. These criteria have been continuously reviewed to enhance specificity and sensitivity in the diagnosis of MS, thereby reducing errors in its differential diagnosis. An important differential diagnosis that shares overlapping features with MS, mainly the progressive forms, are leukodystrophies with demyelination as underlying pathology. Leukodystrophies comprise a rare group of genetically determined disorders that lead to either demyelination or hypomyelination of the central nervous system that can result neuroimaging changes as well as clinical findings similar to those observed in MS. Thus, systematic evaluation encompassing clinical presentation, neuroimaging findings, and laboratory metrics proves indispensable for a differential diagnosis. As such, this study aimed to establish, clearly and objectively, the similarities and differences between MS and the main demyelinating leukodystrophies. The study analyzed the parameters of the McDonald criteria, including clinical, laboratory, and magnetic resonance imaging aspects, as found in patients with leukodystrophies through scoping literature review. The data were compared with the determinations of the revised 2017 McDonald criteria to facilitate the differential diagnosis of these diseases in clinical practice.

Identification and management of subclinical disease activity in early multiple sclerosis: a review

IMPORTANCE

Early treatment initiation in multiple sclerosis (MS) is crucial in preventing irreversible neurological damage and disability progression. The current assessment of disease activity relies on relapse rates and magnetic resonance imaging (MRI) lesion activity, but inclusion of other early, often "hidden," indicators of disease activity may describe a more comprehensive picture of MS.

OBSERVATIONS

Early indicators of MS disease activity other than relapses and MRI activity, such as cognitive impairment, brain atrophy, and fatigue, are not typically captured by routine disease monitoring. Furthermore, silent progression (neurological decline not clearly captured by standard methods) may occur undetected by relapse and MRI lesion activity monitoring. Consequently, patients considered to have no disease activity actually may have worsening disease, suggesting a need to revise MS management strategies with respect to timely initiation and escalation of disease-modifying therapy (DMT). Traditionally, first-line MS treatment starts with low- or moderate-efficacy therapies, before escalating to high-efficacy therapies (HETs) after evidence of breakthrough disease activity. However, multiple observational studies have shown that early initiation of HETs can prevent or reduce disability progression. Ongoing randomized clinical trials are comparing escalation and early HET approaches.

CONCLUSIONS AND RELEVANCE

There is an urgent need to reassess how MS disease activity and worsening are measured. A greater awareness of "hidden" indicators, potentially combined with biomarkers to reveal silent disease activity and neurodegeneration underlying MS, would provide a more complete picture of MS and allow for timely therapeutic intervention with HET or switching DMTs to address suboptimal treatment responses.

Transcranial magnetic stimulation enhances the specificity of multiple sclerosis diagnostic criteria: a critical narrative review

Background

Multiple sclerosis (MS) is an immune-mediated neurodegenerative disease that involves attacks of inflammatory demyelination and axonal damage, with variable but continuous disability accumulation. Transcranial magnetic stimulation (TMS) is a noninvasive method to characterize conduction loss and axonal damage in the corticospinal tract. TMS as a technique provides indices of corticospinal tract function that may serve as putative MS biomarkers. To date, no reviews have directly addressed the diagnostic performance of TMS in MS. The authors aimed to conduct a critical narrative review on the diagnostic performance of TMS in MS.

Methods

The authors searched the Embase, PubMed, Scopus, and Web of Science databases for studies that reported the sensitivity and/or specificity of any reported TMS technique compared to established clinical MS diagnostic criteria. Studies were summarized and critically appraised for their quality and validity.

Results

Seventeen of 1,073 records were included for data extraction and critical appraisal. Markers of demyelination and axonal damage-most notably, central motor conduction time (CMCT)-were specific, but not sensitive, for MS. Thirteen (76%), two (12%), and two (12%) studies exhibited high, unclear, and low risk of bias, respectively. No study demonstrated validity for TMS techniques as diagnostic biomarkers in MS.

Conclusions

CMCT has the potential to: (1) enhance the specificity of clinical MS diagnostic criteria by "ruling in" true-positives, or (2) revise a diagnosis from relapsing to progressive forms of MS. However, there is presently insufficient high-quality evidence to recommend any TMS technique in the diagnostic algorithm for MS.

Erdheim-Chester disease: misdiagnosed as multiple sclerosis

Erdheim-Chester disease is a rare histiocytic neoplasm with a wide range of clinical manifestations. Due to its rarity and protean characteristics, this condition often presents a diagnostic challenge. A Caucasian woman in her late 60s presented with unsteadiness, dysphagia and dysarthria. She was initially diagnosed with secondary progressive multiple sclerosis but deteriorated over 2 years with a potential lack of therapeutic response. Subsequent investigations resulted in the diagnosis of Erdheim-Chester disease. She received targeted therapy with BRAF and MAPK-pathway inhibitors. Her initial response to treatment has been positive with functional gains and reduced disease burden on MR brain imaging, and with no significant adverse effects.

CXCL10/IgG1 Axis in Multiple Sclerosis as a Potential Predictive Biomarker of Disease Activity

BACKGROUND AND OBJECTIVES

Multiple sclerosis (MS) is a heterogeneous disease, and its course is difficult to predict. Prediction models can be established by measuring intrathecally synthesized proteins involved in inflammation, glial activation, and CNS injury.

METHODS

To determine how these intrathecal proteins relate to the short-term, i.e., 12 months, disease activity in relapsing-remitting MS (RRMS), we measured the intrathecal synthesis of 46 inflammatory mediators and 14 CNS injury or glial activation markers in matched serum and CSF samples from 47 patients with MS (pwMS), i.e., 23 RRMS and 24 clinically isolated syndrome (CIS), undergoing diagnostic lumbar puncture. Subsequently, all pwMS were followed for ≥12 months in a retrospective follow-up study and ultimately classified into "active", i.e., developing clinical and/or radiologic disease activity, n = 18) or "nonactive", i.e., not having disease activity, n = 29. Disease activity in patients with CIS corresponded to conversion to RRMS. Thus, patients with CIS were subclassified as "converters" or "nonconverters" based on their conversion status at the end of a 12-month follow-up. Twenty-seven patients with noninflammatory neurologic diseases were included as negative controls. Data were subjected to differential expression analysis and modeling techniques to define the connectivity arrangement (network) between neuroinflammation and CNS injury relevant to short-term disease activity in RRMS.

RESULTS

Lower age and/or higher CXCL13 levels positively distinguished active/converting vs nonactive/nonconverting patients. Network analysis significantly improved the prediction of short-term disease activity because active/converting patients featured a stronger positive connection between IgG1 and CXCL10. Accordingly, analysis of disease activity-free survival demonstrated that pwMS, both RRMS and CIS, with a lower or negative IgG1-CXCL10 correlation, have a higher probability of activity-free survival than the patients with a significant correlation (p < 0.0001, HR ≥ 2.87).

DISCUSSION

Findings indicate that a significant IgG1-CXCL10 positive correlation predicts the risk of short-term disease activity in patients with RRMS and CIS. Thus, the present results can be used to develop a predictive model for MS activity and conversion to RRMS.

The diagnostic value of the central vein sign in radiologically isolated syndrome

OBJECTIVE

The radiologically isolated syndrome (RIS) represents the earliest detectable preclinical phase of multiple sclerosis (MS). Increasing evidence suggests that the central vein sign (CVS) enhances lesion specificity, allowing for greater MS diagnostic accuracy. This study evaluated the diagnostic performance of the CVS in RIS.

METHODS

Patients were prospectively recruited in a single tertiary center for MS care. Participants with RIS were included and compared to a control group of sex and age-matched subjects. All participants underwent 3 Tesla magnetic resonance imaging, including postcontrast susceptibility-based sequences, and the presence of CVS was analyzed. Sensitivity and specificity were assessed for different CVS lesion criteria, defined by proportions of lesions positive for CVS (CVS+) or by the absolute number of CVS+ lesions.

RESULTS

180 participants (45 RIS, 45 MS, 90 non-MS) were included, representing 5285 white matter lesions. Among them, 4608 were eligible for the CVS assessment (970 in RIS, 1378 in MS, and 2260 in non-MS). According to independent ROC comparisons, the proportion of CVS+ lesions performed similarly in diagnosing RIS from non-MS than MS from non-MS (p = 0.837). When a 6-lesion CVS+ threshold was applied, RIS lesions could be diagnosed with an accuracy of 87%. MS could be diagnosed with a sensitivity of 98% and a specificity of 83%. Adding OCBs or Kappa index to CVS biomarker increased the specificity to 100% for RIS diagnosis.

INTERPRETATION

This study shows evidence that CVS is an effective imaging biomarker in differentiating RIS from non-MS, with similar performances to those in MS.

Modelling Disease Progression of Multiple Sclerosis in a South Wales Cohort

OBJECTIVES

The objective of this study was to model multiple sclerosis (MS) disease progression and compare disease trajectories by sex, age of onset, and year of diagnosis.

STUDY DESIGN AND SETTING

Longitudinal EDSS scores (20,854 observations) were collected for 1,787 relapse-onset MS patients at MS clinics in South Wales and modelled using a multilevel model (MLM). The MLM adjusted for covariates (sex, age of onset, year of diagnosis, and disease-modifying treatments), and included interactions between baseline covariates and time variables.

RESULTS

The optimal model was truncated at 30 years after disease onset and excluded EDSS recorded within 3 months of relapse. As expected, older age of onset was associated with faster disease progression at 15 years (effect size (ES): 0.75; CI: 0.63, 0.86; p: <0.001) and female-sex progressed more slowly at 15 years (ES: -0.43; CI: -0.68, -0.18; p: <0.001). Patients diagnosed more recently (defined as 2007-2011 and >2011) progressed more slowly than those diagnosed historically (<2006); (ES: -0.46; CI: -0.75, -0.16; p: 0.006) and (ES: -0.95; CI: -1.20, -0.70; p: <0.001), respectively.

CONCLUSION

We present a novel model of MS outcomes, accounting for the non-linear trajectory of MS and effects of baseline covariates, validating well-known risk factors (sex and age of onset) associated with disease progression. Also, patients diagnosed more recently progressed more slowly than those diagnosed historically.

Recent Advances in Diagnostic, Prognostic, and Disease-Monitoring Biomarkers in Multiple Sclerosis

Multiple sclerosis (MS) is a highly heterogeneous disease. Currently, a combination of clinical features, MRI, and cerebrospinal fluid markers are used in clinical practice for diagnosis and treatment decisions. In recent years, there has been considerable effort to develop novel biomarkers that better reflect the pathologic substrates of the disease to aid in diagnosis and early prognosis, evaluation of ongoing inflammatory activity, detection and monitoring of disease progression, prediction of treatment response, and monitoring of disease-modifying treatment safety. In this review, the authors provide an overview of promising recent developments in diagnostic, prognostic, and disease-monitoring/treatment-response biomarkers in MS.

Diagnostic Performance of Cortical Lesions and the Central Vein Sign in Multiple Sclerosis

Importance

Multiple sclerosis (MS) misdiagnosis remains an important issue in clinical practice.

Objective

To quantify the performance of cortical lesions (CLs) and central vein sign (CVS) in distinguishing MS from other conditions showing brain lesions on magnetic resonance imaging (MRI).

Design, Setting, and Participants

This was a retrospective, cross-sectional multicenter study, with clinical and MRI data acquired between January 2010 and May 2020. Centralized MRI analysis was conducted between July 2020 and December 2022 by 2 raters blinded to participants' diagnosis. Participants were recruited from 14 European centers and from a multicenter pan-European cohort. Eligible participants had a diagnosis of MS, clinically isolated syndrome (CIS), or non-MS conditions; availability of a brain 3-T MRI scan with at least 1 sequence suitable for CL and CVS assessment; presence of T2-hyperintense white matter lesions (WMLs). A total of 1051 individuals were included with either MS/CIS (n = 599; 386 [64.4%] female; mean [SD] age, 41.5 [12.3] years) or non-MS conditions (including other neuroinflammatory disorders, cerebrovascular disease, migraine, and incidental WMLs in healthy control individuals; n = 452; 302 [66.8%] female; mean [SD] age, 49.2 [14.5] years). Five individuals were excluded due to missing clinical or demographic information (n = 3) or unclear diagnosis (n = 2).

Exposures

MS/CIS vs non-MS conditions.

Main Outcomes and Measures

Area under the receiver operating characteristic curves (AUCs) were used to explore the diagnostic performance of CLs and the CVS in isolation and in combination; sensitivity, specificity, and accuracy were calculated for various cutoffs. The diagnostic importance of CLs and CVS compared to conventional MRI features (ie, presence of infratentorial, periventricular, and juxtacortical WMLs) was ranked with a random forest model.

Results

The presence of CLs and the previously proposed 40% CVS rule had a sensitivity, specificity, and accuracy for MS of 59.0% (95% CI, 55.1-62.8), 93.6% (95% CI, 91.4-95.6), and 73.9% (95% CI, 71.6-76.3) and 78.7% (95% CI, 75.5-82.0), 86.0% (95% CI, 82.1-89.5), and 81.5% (95% CI, 78.9-83.7), respectively. The diagnostic performance of the CVS (AUC, 0.89 [95% CI, 0.86-0.91]) was superior to that of CLs (AUC, 0.77 [95% CI, 0.75-0.80]; P < .001), and was increased when combining the 2 imaging markers (AUC, 0.92 [95% CI, 0.90-0.94]; P = .04); in the random forest model, both CVS and CLs outperformed the presence of infratentorial, periventricular, and juxtacortical WMLs in supporting MS differential diagnosis.

Conclusions and Relevance

The findings in this study suggest that CVS and CLs may be valuable tools to increase the accuracy of MS diagnosis.

Multiple sclerosis

Multiple sclerosis remains one of the most common causes of neurological disability in the young adult population (aged 18-40 years). Novel pathophysiological findings underline the importance of the interaction between genetics and environment. Improvements in diagnostic criteria, harmonised guidelines for MRI, and globalised treatment recommendations have led to more accurate diagnosis and an earlier start of effective immunomodulatory treatment than previously. Understanding and capturing the long prodromal multiple sclerosis period would further improve diagnostic abilities and thus treatment initiation, eventually improving long-term disease outcomes. The large portfolio of currently available medications paved the way for personalised therapeutic strategies that will balance safety and effectiveness. Incorporation of cognitive interventions, lifestyle recommendations, and management of non-neurological comorbidities could further improve quality of life and outcomes. Future challenges include the development of medications that successfully target the neurodegenerative aspect of the disease and creation of sensitive imaging and fluid biomarkers that can effectively predict and monitor disease changes.

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

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.

Disease Modifying Strategies in Multiple Sclerosis: New Rays of Hope to Combat Disability?

Multiple sclerosis (MS) is the most prevalent chronic autoimmune inflammatory- demyelinating disorder of the central nervous system (CNS). It usually begins in young adulthood, mainly between the second and fourth decades of life. Usually, the clinical course is characterized by the involvement of multiple CNS functional systems and by different, often overlapping phenotypes. In the last decades, remarkable results have been achieved in the treatment of MS, particularly in the relapsing- remitting (RRMS) form, thus improving the long-term outcome for many patients. As deeper knowledge of MS pathogenesis and respective molecular targets keeps growing, nowadays, several lines of disease-modifying treatments (DMT) are available, an impressive change compared to the relative poverty of options available in the past. Current MS management by DMTs is aimed at reducing relapse frequency, ameliorating symptoms, and preventing clinical disability and progression. Notwithstanding the relevant increase in pharmacological options for the management of RRMS, research is now increasingly pointing to identify new molecules with high efficacy, particularly in progressive forms. Hence, future efforts should be concentrated on achieving a more extensive, if not exhaustive, understanding of the pathogenetic mechanisms underlying this phase of the disease in order to characterize novel molecules for therapeutic intervention. The purpose of this review is to provide a compact overview of the numerous currently approved treatments and future innovative approaches, including neuroprotective treatments as anti-LINGO-1 monoclonal antibody and cell therapies, for effective and safe management of MS, potentially leading to a cure for this disease.

Dynamic changes in patient admission and their disabilities in multiple sclerosis and neuromyelitis optica: A Japanese nationwide administrative data study

BACKGROUND

The real-world data evidences how establishment of neuromyelitis optica (NMO) disease concept and development disease modifying therapy affect the patients with multiple sclerosis (MS) and NMO are lacking. The aim of this study is to clarify the diachronic trend of the severity and admissions of patients with MS and NMO.

METHODS

We retrospectively investigated the trends in admissions, treatments, and disabilities in the patients with MS and NMO using the Japanese administrative data between 2012 and 2017.

RESULTS

We analyzed acute stage 9545 and 2035 admissions in each 6100 MS and 1555 NMO patients. The annual number of admission in MS significantly decreased in 6 years; however, those in NMO consistently increased. The patient proportion with lower disability was significantly increased in MS and NMO. These trends were especially observed in patients admitted to centralized hospitals with more active treatments, such as second-line disease modifying therapy for MS and plasmapheresis for NMO. Patients with NMO using DMT for MS diminished in 6 years.

CONCLUSION

A gradual improvement of disability in patients with MS and NMO was observed, probably due to advanced treatments, increased NMO awareness, and decreased misdiagnosis, which seems to be the key for better prognosis in MS and NMO.

M-DDC: MRI based demyelinative diseases classification with U-Net segmentation and convolutional network

Childhood demyelinative diseases classification (DDC) with brain magnetic resonance imaging (MRI) is crucial to clinical diagnosis. But few attentions have been paid to DDC in the past. How to accurately differentiate pediatric-onset neuromyelitis optica spectrum disorder (NMOSD) from acute disseminated encephalomyelitis (ADEM) based on MRI is challenging in DDC. In this paper, a novel architecture M-DDC based on joint U-Net segmentation network and deep convolutional network is developed. The U-Net segmentation can provide pixel-level structure information, that helps the lesion areas location and size estimation. The classification branch in DDC can detect the regions of interest inside MRIs, including the white matter regions where lesions appear. The performance of the proposed method is evaluated on MRIs of 201 subjects recorded from the Children's Hospital of Zhejiang University School of Medicine. The comparisons show that the proposed DDC achieves the highest accuracy of 99.19% and dice of 71.1% for ADEM and NMOSD classification and segmentation, respectively.

Identification of demyelinating lesions and application of McDonald criteria when confronted with white matter lesions on brain MRI

INTRODUCTION

White matter lesions (WML) on magnetic resonance imaging (MRI) are common in clinical practice. When analyzing WML, radiologists sometimes propose a pathophysiological mechanism to explain the observed MRI abnormalities, which can be a source of anxiety for patients. In some cases, discordance may appear between the patient's clinical symptoms and the identification of the MRI-appearing WML, leading to extensive diagnostic work-up. To avoid misdiagnosis, the analysis of WML should be standardized, and a consensual MRI reading approach is needed.

OBJECTIVE

To analyze the MRI WML identification process, associated diagnosis approach, and misinterpretations in physicians involved in WML routine practice.

METHODS

Through a survey distributed online to practitioners involved in WML diagnostic work-up, we described the leading causes of MRI expertise misdiagnosis and associated factors: clinical experience, physicians' subspecialty and location of practice, and type of device used to complete the survey. The survey consisted of sixteen T2-weighted images MRI analysis, from which ten were guided (binary response to lesion location identification), four were not shown (multiple possible answers), and two were associated with dissemination in space (DIS) McDonald criteria application. Two independent, experienced practitioners determined the correct answers before the participants' completion.

RESULTS

In total, 364 participants from the French Neuro Radiological (SFNR), French Neurological (SFN), and French Multiple Sclerosis (SFSEP) societies completed the survey entirely. According to lesion identification, 34.3% and 16.9% of the participants correctly identified juxtacortical and periventricular lesions, respectively, whereas 56.3% correctly identified non-guided lesions. Application of the 2017 McDonald's DIS criteria was correct for 35.3% of the participants. According to the global survey scoring, factors independently associated with correct answers in multivariate analysis were MS-expert subspecialty (P<0.001), young clinical practitioners (P=0.02), and the use of a computer instead of a smartphone to perform WML analysis (P=0.03).

CONCLUSION

Our results highlight the difficulties regarding WML analysis in clinical practice and suggest that radiologists and neurologists should rely on each other to ensure the diagnosis of multiple sclerosis and related disorders and limit misdiagnoses.

Real-world challenges in the diagnosis of primary progressive multiple sclerosis

BACKGROUND AND PURPOSE

Despite the 2017 revisions to the McDonald criteria, diagnosing primary progressive multiple sclerosis (PPMS) remains challenging. To improve clinical practice, the aim was to identify frequent diagnostic challenges in a real-world setting and associate these with the performance of the 2010 and 2017 PPMS diagnostic McDonald criteria.

METHODS

Clinical, radiological and laboratory characteristics at the time of diagnosis were retrospectively recorded from designated PPMS patient files. Possible complicating factors were recorded such as confounding comorbidity, signs indicative of alternative diagnoses, possible earlier relapses and/or incomplete diagnostic work-up (no cerebrospinal fluid examination and/or magnetic resonance imaging brain and spinal cord). The percentages of patients fulfilling the 2010 and 2017 McDonald criteria were calculated after censoring patients with these complicating factors.

RESULTS

A total of 322 designated PPMS patients were included. Of all participants, it was found that n = 28/322 had confounding comorbidity and/or signs indicative of alternative diagnoses, n = 103/294 had possible initial relapsing and/or uncertainly progressive phenotypes and n = 73/191 received an incomplete diagnostic work-up. When applying the 2010 and 2017 diagnostic PPMS McDonald criteria on n = 118 cases with a full diagnostic work-up and a primary progressive disease course without a better alternative explanation, these were met by 104/118 (88.1%) and 98/118 remaining patients (83.1%), respectively (p = 0.15).

CONCLUSION

Accurate interpretation of the initial clinical course, consideration of alternative diagnoses and a full diagnostic work-up are the cornerstones of a PPMS diagnosis. When these conditions are met, the 2010 and 2017 McDonald criteria for PPMS perform similarly, emphasizing the importance of their appropriate application in clinical practice.

The Potential Role of Fecal Microbiota Transplant in the Reversal or Stabilization of Multiple Sclerosis Symptoms: A Literature Review on Efficacy and Safety

Multiple sclerosis (MS) affects millions of people worldwide, and recent data have identified the potential role of the gut microbiome in inducing autoimmunity in MS patients. To investigate the potential of fecal microbiota transplant (FMT) as a treatment option for MS, we conducted a comprehensive literature search (PubMed/Medline, Embase, Web of Science, Scopus, and Cochrane) and identified five studies that involved 15 adult MS patients who received FMT for gastrointestinal symptoms. The primary outcome of this review was to assess the effect of FMT in reversing and improving motor symptoms in MS patients, while the secondary outcome was to evaluate the safety of FMT in this patient population. Our findings suggest that all 15 patients who received FMT experienced improved and reversed neurological symptoms secondary to MS. This improvement was sustained even in follow-up years, with no adverse effects observed. These results indicate that FMT may hold promise as a treatment option for MS, although further research is necessary to confirm these findings.

Excess weight increases the risk of sarcopenia in patients with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease. Nutritional status influences the course of the disease, however, its relationship with sarcopenia needs further investigation. The aim of the study was to identify patients with sarcopenia and assess its association with nutritional status and the clinical course of the disease.

METHODS

The study assessed 110 patients submitted to evaluation of sociodemographic characteristics, level of physical activity, nutritional status, and presence of sarcopenia. The clinical course of the disease, age at onset, disease duration, disease-modifying therapy, and expanded scale of disability status (EDSS) were investigated.

RESULTS

Mean age was 37.17 (SD = 10.60) years, disease duration was 6.29 years (SD = 4.65), with a predominance of female gender (80.90 %), relapsing-remitting clinical form (RRMS) (89.10 %) and mild level of disability (EDSS median = 1.92). The group had excess weight (53.6 %) according to body mass index (BMI) and abdominal fat accumulation measured by waist circumference (WC) (53.6 %). High percentage of fat mass ( % FM) was observed in 54.5 % and 38.2 % of the patients according to bioimpedance (BIA) and ultrasound (US), respectively. It was observed that 15.5 % were at risk for sarcopenia, which was associated with excess weight, and high % FM (p<0.05).

CONCLUSION

These findings highlight the importance of including nutritional status indicators, and sarcopenia assessment in the care of patients with MS.

Get With the Guidelines on MS Imaging by Leveraging Peer Learning

OBJECTIVES

To achieve consensus on the performance, interpretation and reporting of MS imaging according to up-to-date guidelines using the Peer Learning Methodology.

MATERIALS AND METHODS

We utilized the Peer Learning Methodology to engage our clinical and radiology colleagues, review the current guidelines, acheive consensus on imaging techniques and reporting standards. After implementing changes, we collected radiologist feedback on the impact of the optimized images on their interpretation.

RESULTS

Survey responders indicated a strong preference for the new protocol in terms of overall image quality, individual lesions conspicuity and confidence in the ability to detect an MS lesion. The new protocol was preferred for both MS diagnosis and MS surveillance in 25 of 28 responses.

CONCLUSION

The Peer Learning Methodology is an effective tool to standardize and improve MR imaging quality, interpretation and reporting for Multiple Sclerosis in accordance with current guidelines.

Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorder using a deep learning model

Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are autoimmune inflammatory disorders of the central nervous system (CNS) with similar characteristics. The differential diagnosis between MS and NMOSD is critical for initiating early effective therapy. In this study, we developed a deep learning model to differentiate between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) using brain magnetic resonance imaging (MRI) data. The model was based on a modified ResNet18 convolution neural network trained with 5-channel images created by selecting five 2D slices of 3D FLAIR images. The accuracy of the model was 76.1%, with a sensitivity of 77.3% and a specificity of 74.8%. Positive and negative predictive values were 76.9% and 78.6%, respectively, with an area under the curve of 0.85. Application of Grad-CAM to the model revealed that white matter lesions were the major classifier. This compact model may aid in the differential diagnosis of MS and NMOSD in clinical practice.

Absence of Oligoclonal Bands in Multiple Sclerosis: A Call for Differential Diagnosis

BACKGROUND

Immunoglobulin gamma (IgG) oligoclonal bands (OCB) in the cerebrospinal fluid (CSF) are absent in a small group of multiple sclerosis (MS) patients. According to previous research, OCB-negative MS patients differ genetically but not clinically from OCB-positive MS patients. However, whether OCB-negative MS is a unique immunological and clinical entity remains unclear. The absence of OCB poses a significant challenge in diagnosing MS. (1) Objective: The objective of this study was twofold: (1) to determine the prevalence of OCB-negative MS patients in the Uppsala region, and (2) to assess the frequency of misdiagnosis in this patient group. (2) Methods: We conducted a retrospective study using data from the Swedish MS registry (SMSreg) covering 83% of prevalent MS cases up to 20 June 2020 to identify all MS patients in the Uppsala region. Subsequently, we collected relevant information from the medical records of all OCB-negative MS cases, including age of onset, gender, presenting symptoms, MRI features, phenotype, Expanded Disability Status Scale (EDSS) scores, and disease-modifying therapies (DMTs). (3) Results: Out of 759 MS patients identified, 69 had an OCB-negative MS diagnosis. Upon re-evaluation, 46 patients had a typical history and MRI findings of MS, while 23 had unusual clinical and/or radiologic features. An alternative diagnosis was established for the latter group, confirming the incorrectness of the initial MS diagnosis. The average EDSS score was 2.0 points higher in the MS group than in the non-MS group (p = 0.001). The overall misdiagnosis rate in the cohort was 33%, with 22% of misdiagnosed patients having received DMTs. (4) Conclusions: Our results confirm that the absence of OCB in the CSF should raise suspicion of possible misdiagnosis in MS patients and prompt a diagnostic reassessment.

Current Updates on the Diagnosis and Management of Multiple Sclerosis for the General Neurologist

Multiple sclerosis (MS) is an immune-driven disease that affects the central nervous system and is characterized by acute-on-chronic demyelination attacks. It is a major cause of global neurological disability, and its prevalence has increased in the United States. Conceptual understandings of MS have evolved over time, including the identification of B cells as key factors in its pathophysiology. The foundation of MS management involves preventing flares so as to avoid long-term functional decline. Treatments may be categorized into low-, middle-, and high-efficacy medications based on their efficacy in relapse prevention. With 24 FDA-approved treatments for MS, individual therapy is chosen based on distinct mechanisms and potential side effects. This review provides a detailed update on the epidemiology, diagnosis, treatment advances, and major ongoing research investigations in MS.

Central vein sign and diffusion MRI differentiate microstructural features within white matter lesions of multiple sclerosis patients with comorbidities

Introduction

The Central Vein Sign (CVS) has been suggested as a potential biomarker to improve diagnostic specificity in multiple sclerosis (MS). Nevertheless, the impact of comorbidities on CVS performance has been poorly investigated so far. Despite the similar features shared by MS, migraine and Small Vessel Disease (SVD) at T2-weighted conventional MRI sequences, ex-vivo studies demonstrated their heterogeneous histopathological substrates. If in MS, inflammation, primitive demyelination and axonal loss coexist, in SVD demyelination is secondary to ischemic microangiopathy, while the contemporary presence of inflammatory and ischemic processes has been suggested in migraine. The aims of this study were to investigate the impact of comorbidities (risk factors for SVD and migraine) on the global and subregional assessment of the CVS in a large cohort of MS patients and to apply the Spherical Mean Technique (SMT) diffusion model to evaluate whether perivenular and non-perivenular lesions show distinctive microstructural features.

Methods

120 MS patients stratified into 4 Age Groups performed 3T brain MRI. WM lesions were classified in "perivenular" and "non-perivenular" by visual inspection of FLAIR* images; mean values of SMT metrics, indirect estimators of inflammation, demyelination and fiber disruption (EXTRAMD: extraneurite mean diffusivity, EXTRATRANS: extraneurite transverse diffusivity and INTRA: intraneurite signal fraction, respectively) were extracted.

Results

Of the 5303 lesions selected for the CVS assessment, 68.7% were perivenular. Significant differences were found between perivenular and non-perivenular lesion volume in the whole brain (p < 0.001) and between perivenular and non-perivenular lesion volume and number in all the four subregions (p < 0.001 for all). The percentage of perivenular lesions decreased from youngest to oldest patients (79.7%-57.7%), with the deep/subcortical WM of oldest patients as the only subregion where the number of non-perivenular was higher than the number of perivenular lesions. Older age and migraine were independent predictors of a higher percentage of non-perivenular lesions (p < 0.001 and p = 0.013 respectively). Whole brain perivenular lesions showed higher inflammation, demyelination and fiber disruption than non perivenular lesions (p = 0.001, p = 0.001 and p = 0.02 for EXTRAMD, EXTRATRANS and INTRA respectively). Similar findings were found in the deep/subcortical WM (p = 0.001 for all). Compared to non-perivenular lesions, (i) perivenular lesions located in periventricular areas showed a more severe fiber disruption (p = 0.001), (ii) perivenular lesions located in juxtacortical and infratentorial regions exhibited a higher degree of inflammation (p = 0.01 and p = 0.05 respectively) and (iii) perivenular lesions located in infratentorial areas showed a higher degree of demyelination (p = 0.04).

Discussion

Age and migraine have a relevant impact in reducing the percentage of perivenular lesions, particularly in the deep/subcortical WM. SMT may differentiate perivenular lesions, characterized by higher inflammation, demyelination and fiber disruption, from non perivenular lesions, where these pathological processes seemed to be less pronounced. The development of new non-perivenular lesions, especially in the deep/subcortical WM of older patients, should be considered a "red flag" for a different -other than MS- pathophysiology.

Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia

Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%.

The Importance of Optical Coherence Tomography in the Diagnosis of Atypical or Subclinical Optic Neuritis: A Case Series Study

Background: Optic neuritis (ON) is an inflammatory condition of the optic nerve. ON is associated with development of demyelinating diseases of the central nervous system (CNS). CNS lesions visualized by magnetic resonance imaging (MRI) and the finding of oligoclonal IgG bands (OB) in the cerebrospinal fluid (CSF) are used to stratify the risk of MS after a "first" episode of ON. However, the diagnosis of ON in absence of typical clinical manifestations can be challenging. Methods and Materials: Here we present three cases with changes in the optic nerve and ganglion cell layer in the retina over the disease course. (1) A 34-year-old female with a history of migraine and hypertension had suspect amaurosis fugax (transient vision loss) in the right eye. This patient developed MS four years later. Optical coherence tomography (OCT) showed dynamic changes of the thickness of peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) over time. (2) A 29-year-old male with spastic hemiparesis and lesions in the spinal cord and brainstem. Six years later he showed bilateral subclinical ON identified using OCT, visual evoked potentials (VEP) and MRI. The patient fulfilled diagnosis criteria of seronegative neuromyelitis optica (NMO). (3) A 23-year-old female with overweight and headache had bilateral optic disc swelling. With OCT and lumbar puncture, idiopathic intracranial hypertension (IIH) was excluded. Further investigation showed positive antibody for myelin oligodendrocyte glycoprotein (MOG). Conclusions: These three cases illustrate the importance of using OCT to facilitate quick, objective and accurate diagnosis of atypical or subclinical ON, and thus proper therapy.

Frequency of NMOSD misdiagnosis in a cohort from Latin America: Impact and evaluation of different contributors

BACKGROUND

Neuromyelitis optica spectrum disorder (NMOSD) misdiagnosis (i.e. the incorrect diagnosis of patients who truly have NMOSD) remains an issue in clinical practice. We determined the frequency and factors associated with NMOSD misdiagnosis in patients evaluated in a cohort from Latin America.

METHODS

We retrospectively reviewed the medical records of patients with NMOSD, according to the 2015 diagnostic criteria, from referral clinics in six Latin American countries (Argentina, Chile, Paraguay, Colombia, Ecuador, and Venezuela). Diagnoses prior to NMOSD and ultimate diagnoses, demographic, clinical and paraclinical data, and treatment schemes were evaluated.

RESULTS

A total of 469 patients presented with an established diagnosis of NMOSD (73.2% seropositive) and after evaluation, we determined that 56 (12%) patients had been initially misdiagnosed with a disease other than NMOSD. The most frequent alternative diagnoses were multiple sclerosis (MS; 66.1%), clinically isolated syndrome (17.9%), and cerebrovascular disease (3.6%). NMOSD misdiagnosis was determined by MS/NMOSD specialists in 33.9% of cases. An atypical MS syndrome was found in 86% of misdiagnosed patients, 50% had NMOSD red flags in brain and/or spinal magnetic resonance imaging (MRI), and 71.5% were prescribed disease-modifying drugs.

CONCLUSIONS

NMOSD misdiagnosis is relatively frequent in Latin America (12%). Misapplication and misinterpretation of clinical and neuroradiological findings are relevant factors associated with misdiagnosis.

Cerebrospinal Fluid Biomarkers in Differential Diagnosis of Multiple Sclerosis and Systemic Inflammatory Diseases with Central Nervous System Involvement

BACKGROUND

Diagnosis of multiple sclerosis (MS) is established on criteria according to clinical and radiological manifestation. Cerebrospinal fluid (CSF) analysis is an important part of differential diagnosis of MS and other inflammatory processes in the central nervous system (CNS).

METHODS

In total, 242 CSF samples were collected from patients undergoing differential MS diagnosis because of the presence of T2-hyperintensive lesions on brain MRI. The non-MS patients were subdivided into systemic inflammatory diseases with CNS involvement (SID) or cerebrovascular diseases (CVD) or other non-inflammatory diseases (NID). All samples were analyzed for the presence of oligoclonal bands and ELISA was performed for detection of: INF gamma, IL-6, neurofilaments light chain (NF-L), GFAP, CHI3L1, CXCL13, and osteopontin.

RESULTS

The level of IL-6 (p = 0.024), osteopontin (p = 0.0002), and NF-L (p = 0.002) was significantly different among groups. IL-6 (p = 0.0350) and NF-L (p = 0.0015) level was significantly higher in SID compared to NID patients. A significantly higher level of osteopontin (p = 0.00026) and NF-L (p = 0.002) in MS compared to NID population was noted. ROC analysis found weak diagnostic power for osteopontin and NFL-L.

CONCLUSIONS

The classical and non-standard markers of inflammatory process and neurodegeneration do not allow for sufficient differentiation between MS and non-MS inflammatory CNS disorders. Weak diagnostic power observed for the osteopontin and NF-L needs to be further investigated.

Kappa Free Light Chain Biomarkers Are Efficient for the Diagnosis of Multiple Sclerosis

Background and Objectives

Kappa free light chains (KFLC) seem to efficiently diagnose MS. However, extensive cohort studies are lacking to establish consensus cut-offs, notably to rule out non-MS autoimmune CNS disorders. Our objectives were to (1) determine diagnostic performances of CSF KFLC, KFLC index, and KFLC intrathecal fraction (IF) threshold values that allow us to separate MS from different CNS disorder control populations and compare them with oligoclonal bands' (OCB) performances and (2) to identify independent factors associated with KFLC quantification in MS.

Methods

We conducted a retrospective multicenter study involving 13 French MS centers. Patients were included if they had a noninfectious and nontumoral CNS disorder, eligible data concerning CSF and serum KFLC, albumin, and OCB. Patients were classified into 4 groups according to their diagnosis: MS, clinically isolated syndrome (CIS), other inflammatory CNS disorders (OIND), and noninflammatory CNS disorder controls (NINDC).

Results

One thousand six hundred twenty-one patients were analyzed (675 MS, 90 CIS, 297 OIND, and 559 NINDC). KFLC index and KFLC IF had similar performances in diagnosing MS from nonselected controls and OIND (p= 0.123 andp= 0.991 for area under the curve [AUC] comparisons) and performed better than CSF KFLC (p< 0.001 for all AUC comparisons). A KFLC index of 8.92 best separated MS/CIS from the entire nonselected control population, with better performances than OCB (p< 0.001 for AUC comparison). A KFLC index of 11.56 best separated MS from OIND, with similar performances than OCB (p= 0.065). In the multivariate analysis model, female gender (p= 0.003), young age (p= 0.013), and evidence of disease activity (p< 0.001) were independent factors associated with high KFLC index values in patients with MS, whereas MS phenotype, immune-modifying treatment use at sampling, and the FLC analyzer type did not influence KFLC index.

Discussion

KFLC biomarkers are efficient tools to separate patients with MS from controls, even when compared with other patients with CNS autoimmune disorder. Given these results, we suggest using KFLC index or KFLC IF as a criterion to diagnose MS.

Classification of Evidence

This study provides Class III evidence that KFLC index or IF can be used to differentiate patients with MS from nonselected controls and from patients with other autoimmune CNS disorders.

Present and future of the diagnostic work-up of multiple sclerosis: the imaging perspective

In recent years, the use of magnetic resonance imaging (MRI) for the diagnostic work-up of multiple sclerosis (MS) has evolved considerably. The 2017 McDonald criteria show high sensitivity and accuracy in predicting a second clinical attack in patients with a typical clinically isolated syndrome and allow an earlier diagnosis of MS. They have been validated, are evidence-based, simplify the clinical use of MRI criteria and improve MS patients' management. However, to limit the risk of misdiagnosis, they should be applied by expert clinicians only after the careful exclusion of alternative diagnoses. Recently, new MRI markers have been proposed to improve diagnostic specificity for MS and reduce the risk of misdiagnosis. The central vein sign and chronic active lesions (i.e., paramagnetic rim lesions) may increase the specificity of MS diagnostic criteria, but further effort is necessary to validate and standardize their assessment before implementing them in the clinical setting. The feasibility of subpial demyelination assessment and the clinical relevance of leptomeningeal enhancement evaluation in the diagnostic work-up of MS appear more limited. Artificial intelligence tools may capture MRI attributes that are beyond the human perception, and, in the future, artificial intelligence may complement human assessment to further ameliorate the diagnostic work-up and patients' classification. However, guidelines that ensure reliability, interpretability, and validity of findings obtained from artificial intelligence approaches are still needed to implement them in the clinical scenario. This review provides a summary of the most recent updates regarding the application of MRI for the diagnosis of MS.

Improvement in time to multiple sclerosis diagnosis: 25-year retrospective analysis from New York State MS Consortium (NYSMSC)

Judicious multiple sclerosis (MS) diagnosis and early start of disease modifying therapy significantly improves long-term disability outcomes in persons with MS (pwMS). Retrospective analysis based on 25-year New York State MS Consortium (NYSMSC) data determined the effect of changes in the respective diagnostic criteria in shortening the time between symptom onset to MS diagnosis. Based on 9378 current and historical MS cases, there was a significant decrease in time to diagnosis in pwMS from 1982–2001 to >2017 periods (average 4.2 vs. 1.1 years, p < 0.001). Additional improvements and better implementation of the MS diagnostic criteria can further decrease the diagnosis lag.

Leistungsfähigkeit der McDonald-Kriterien von 2017

Hintergrund

Die schnelle und zuverlässige Diagnose einer Multiplen Sklerose (MS) ist entscheidend, um eine angepasste verlaufsmodifizierende Therapie zu beginnen. Die 2017-Revision der McDonald-Kriterien hat das Ziel, eine einfachere und frühzeitigere MS-Diagnose mit hoher diagnostischer Genauigkeit zu ermöglichen.

Ziel der Arbeit/Fragestellung

In der vorliegenden Arbeit wurden die publizierten Arbeiten, die die Anwendung der McDonald-Kriterien von 2017 und 2010 miteinander verglichen haben, ausgewertet und bezüglich der diagnostischen Leistungsfähigkeit analysiert.

Material und Methoden

Mittels Literaturrecherche in der PubMed-Datenbank (Suchbegriff: McDonald criteria 2010 and McDonald criteria 2017) wurden 20 Studien und ein Übersichtsartikel mit insgesamt 3006 auswertbaren Patienten identifiziert.

Ergebnisse

Bei Anwendung der McDonald-Kriterien von 2017 konnte die Diagnose einer MS bei mehr Patienten (2277/3006 Patienten, 76 %) und in einem früheren Stadium (3–10 Monate) verglichen mit der Revision von 2010 (1562/3006 Patienten, 52 %) gestellt werden. Von den zusätzlichen MS-Diagnosen sind 193/715 auf die Anpassung der bildgebenden Kriterien der zeitlichen Dissemination und 536/715 auf die Einführung der oligoklonalen Banden als diagnostisches Kriterium zurückführen.

Diskussion

Die revidierten McDonald-Kriterien von 2017 erlauben die Diagnosestellung einer MS bei einem höheren Anteil an Patienten beim ersten klinischen Ereignis.

Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: Emerging machine learning techniques and future avenues

The current diagnostic criteria for multiple sclerosis (MS) lack specificity, and this may lead to misdiagnosis, which remains an issue in present-day clinical practice. In addition, conventional biomarkers only moderately correlate with MS disease progression. Recently, some MS lesional imaging biomarkers such as cortical lesions (CL), the central vein sign (CVS), and paramagnetic rim lesions (PRL), visible in specialized magnetic resonance imaging (MRI) sequences, have shown higher specificity in differential diagnosis. Moreover, studies have shown that CL and PRL are potential prognostic biomarkers, the former correlating with cognitive impairments and the latter with early disability progression. As machine learning-based methods have achieved extraordinary performance in the assessment of conventional imaging biomarkers, such as white matter lesion segmentation, several automated or semi-automated methods have been proposed as well for CL, PRL, and CVS. In the present review, we first introduce these MS biomarkers and their imaging methods. Subsequently, we describe the corresponding machine learning-based methods that were proposed to tackle these clinical questions, putting them into context with respect to the challenges they are facing, including non-standardized MRI protocols, limited datasets, and moderate inter-rater variability. We conclude by presenting the current limitations that prevent their broader deployment and suggesting future research directions.

Multiple sclerosis in patients with hereditary spastic paraplegia: a case report and systematic review

INTRODUCTION

An increasing number of cases of comorbid hereditary spastic paraplegia (HSP) and multiple sclerosis (MS) have been described. We report a patient with the SPG3A form of HSP and features of relapsing-remitting MS (RRMS). We took this opportunity to review the current literature of co-occurring MS and HSP.

METHOD

The patient underwent clinical, laboratory and neuroimaging evaluations. We performed a literature search for cases of HSP and MS. The 2017 McDonalds Criteria for MS were retrospectively applied to the selected cases.

RESULTS

A 34-year-old woman, presenting a molecular diagnosis of SPG3A, complained subacute sensory-motor symptoms. Spinal MRI disclosed T2-hyperintense lesions at C2, T6 and T4 level, the latter presenting contrast-enhancement. CSF analysis showed oligoclonal bands. She was treated with intravenous high-dose steroids, with symptom resolution. The literature review yielded 13 papers reporting 20 possible cases of MS and HSP. Nine patients (5 M, median age 34) met the 2017 McDonald criteria. Five (25%) received a diagnosis of RRMS and four (20%) of primary progressive MS. Brain MRI showed multiple WM lesions, mostly periventricular. Six of seven cases (85.7%) had spinal cord involvement. Oligoclonal bands were found in 6/8 (75%) patients. Seven patients (77.7%) improved/stabilized on immunotherapy.

CONCLUSION

This is the first description on the association between SPG3A type of HSP and MS. This report adds to the other reported cases of co-occurring HSPs and MS. Although it remains unclear if this association is casual or causal, clinicians should be aware that an HSP diagnosis does not always exclude a concomitant MS.

Differentiating central nervous system demyelinating disorders: The role of clinical, laboratory, imaging characteristics and peripheral blood type I interferon activity

Objective: While multiple sclerosis (MS) is considered the cornerstone of autoimmune demyelinating CNS disorders, systemic autoimmune diseases (SADs) are important MS mimickers. We sought to explore whether distinct clinical, laboratory, and imaging characteristics along with quantitation of peripheral blood type I interferon (IFN) activity could aid in differentiating between them.

Methods: A total of 193 consecutive patients with imaging features suggesting the presence of CNS demyelinating disease with or without relevant clinical manifestations underwent full clinical, laboratory, and imaging evaluation, including testing for specific antibodies against 15 cellular antigens. Expression analysis of type I IFN-inducible genes (MX-1, IFIT-1, and IFI44) was performed by real-time PCR, and a type I IFN score, reflecting type I IFN peripheral activity, was calculated. After joint neurological/rheumatological evaluation and 1 year of follow-up, patients were classified into MS spectrum and CNS autoimmune disorders.

Results: While 66.3% (n = 128) of the patients were diagnosed with MS spectrum disorders (predominantly relapsing–remitting MS), 24.9% (n = 48) were included in the CNS autoimmune group, and out of those, one-fourth met the criteria for SAD (6.7% of the cohort, n = 13); the rest (18.1% of the cohort, n = 35), despite showing evidence of systemic autoimmunity, did not fulfill SAD criteria and comprised the “demyelinating disease with autoimmune features” (DAF) subgroup. Compared to the MS spectrum, CNS autoimmune patients were older, more frequently females, with increased rates of hypertension/hyperlipidemia, family history of autoimmunity, cortical dysfunction, anti-nuclear antibody titers ≥1/320, anticardiolipin IgM positivity, and atypical for MS magnetic resonance imaging lesions. Conversely, lower rates of infratentorial and callosal MRI lesions, CSF T2 oligoclonal bands, and IgG-index positivity were observed in CNS autoimmune patients. Patients fulfilling SAD criteria, but not the DAF group, had significantly higher peripheral blood type I IFN scores at baseline compared to MS spectrum [median (IQR)]: 50.18 (152.50) vs. −0.64 (6.75), p-value: 0.0001.

Conclusion: Our study suggests that underlying systemic autoimmunity is not uncommon in patients evaluated for possible CNS demyelination. Distinct clinical, imaging and laboratory characteristics can aid in early differentiation between MS and CNS-involving systemic autoimmunity allowing for optimal therapeutic strategies. Activated type I IFN pathway could represent a key mediator among MS-like-presenting SADs and therefore a potential therapeutic target.

NMOSD—Diagnostic Dilemmas Leading towards Final Diagnosis

(1) Background: The emergence of white matter lesions in the central nervous system (CNS) can lead to diagnostic dilemmas. They are a common radiological symptom and their patterns may overlap CNS or systemic diseases and provoke underdiagnosis or misdiagnosis. The aim of the study was to assess factors influencing the underdiagnosis of neuromyelitis optica spectrum disorder (NMOSD) as well as to estimate NMOSD epidemiology in Lubelskie voivodeship, Poland. (2) Methods: This retrospective study included 1112 patients, who were made a tentative or an established diagnosis of acute or subacute onset of neurological deficits. The evaluation was based on medical history, neurological examination, laboratory and radiographic results and fulfilment of diagnosis criteria. (3) Results: Up to 1.62 percent of patients diagnosed with white matter lesions and up to 2.2% of the patients previously diagnosed with MS may suffer from NMOSD. The duration of delayed diagnosis is longer for males, despite the earlier age of onset. Seropositive cases for antibodies against aquaporin-4 have worse prognosis for degree of disability. (4) Conclusions: Underdiagnosis or misdiagnosis in NMOSD still remains a problem in clinical practice and has important implications for patients. The incorrect diagnosis is caused by atypical presentation or NMOSD-mimics; however, covariates such as gender, onset and diagnosis age may also have an influence.

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy

The eye is one of the most important organs of the human body. Eye diseases are closely related to other systemic diseases, both of which influence each other. Numerous systemic diseases lead to special clinical manifestations and complications in the eyes. Typical diseases include diabetic retinopathy, hypertensive retinopathy, thyroid associated ophthalmopathy, optic neuromyelitis, and Behcet's disease. Systemic disease-related ophthalmopathy is usually a chronic disease, and the analysis of imaging markers is helpful for a comprehensive diagnosis of these diseases. Recently, artificial intelligence (AI) technology based on deep learning has rapidly developed, leading to numerous achievements and arousing widespread concern. Presently, AI technology has made significant progress in research on imaging markers of systemic disease-related ophthalmopathy; however, there are also many limitations and challenges. This article reviews the research achievements, limitations, and future prospects of AI image analysis technology in systemic disease-related ophthalmopathy.

Lesion size and shape in central vein sign assessment for multiple sclerosis diagnosis: An in vivo and postmortem MRI study

BACKGROUND

The "central vein sign" (CVS), a linear hypointensity on T2*-weighted imaging corresponding to a central vein/venule, is associated with multiple sclerosis (MS) lesions. The effect of lesion-size exclusion criteria on MS diagnostic accuracy has not been extensively studied.

OBJECTIVE

Investigate the optimal lesion-size exclusion criteria for CVS use in MS diagnosis.

METHODS

Cross-sectional study of 163 MS and 51 non-MS, and radiological/histopathological correlation of 5 MS and 1 control autopsy cases. The effects of lesion-size exclusion on MS diagnosis using the CVS, and intralesional vein detection on histopathology were evaluated.

RESULTS

CVS+ lesions were larger compared to CVS- lesions, with effect modification by MS diagnosis (mean difference +7.7 mm³, p = 0.004). CVS percentage-based criteria with no lesion-size exclusion showed the highest diagnostic accuracy in differentiating MS cases. However, a simple count of three or more CVS+ lesions greater than 3.5 mm is highly accurate and can be rapidly implemented (sensitivity 93%; specificity 88%). On magnetic resonance imaging (MRI)-histopathological correlation, the CVS had high specificity for identifying intralesional veins (0/7 false positives).

CONCLUSION

Lesion-size measures add important information when using CVS+ lesion counts for MS diagnosis. The CVS is a specific biomarker corresponding to intralesional veins on histopathology.

Cognitive Decline in Older People with Multiple Sclerosis—A Narrative Review of the Literature

Several important questions regarding cognitive aging and dementia in older people with multiple sclerosis (PwMS) are the focus of this narrative review: Do older PwMS have worse cognitive decline compared to older people without MS? Can older PwMS develop dementia or other neurodegenerative diseases such as Alzheimer’s disease (AD) that may be accelerated due to MS? Are there any potential biomarkers that can help to determine the etiology of cognitive decline in older PwMS? What are the neural and cellular bases of cognitive aging and neurodegeneration in MS? Current evidence suggests that cognitive impairment in MS is distinguishable from that due to other neurodegenerative diseases, although older PwMS may present with accelerated cognitive decline. While dementia is prevalent in PwMS, there is currently no consensus on defining it. Cerebrospinal fluid and imaging biomarkers have the potential to identify disease processes linked to MS and other comorbidities—such as AD and vascular disease—in older PwMS, although more research is required. In conclusion, one should be aware that multiple underlying pathologies can coexist in older PwMS and cause cognitive decline. Future basic and clinical research will need to consider these complex factors to better understand the underlying pathophysiology, and to improve diagnostic accuracy.

Confirming a Historical Diagnosis of Multiple Sclerosis: Challenges and Recommendations

Patients with a historical diagnosis of multiple sclerosis (MS)-a patient presenting with a diagnosis of MS made previously and by a different clinician-present specific diagnostic and therapeutic challenges in clinical practice. Application of the McDonald criteria is most straightforward when applied contemporaneously with a syndrome typical of an MS attack or relapse; however, retrospective application of the criteria in some patients with a historical diagnosis of MS can be problematic. Limited patient recollection of symptoms and evolution of neurologic examination and MRI findings complicate confirmation of an earlier MS diagnosis and assessment of subsequent disease activity or clinical progression. Adequate records for review of prior clinical examinations, laboratory results, and/or MRI scans obtained at the time of diagnosis or during ensuing care may be inadequate or unavailable. This article provides recommendations for a clinical approach to the evaluation of patients with a historical diagnosis of MS to aid diagnostic confirmation, avoid misdiagnosis, and inform therapeutic decision making.

Increasing incidence and prevalence of multiple sclerosis in the Greater Hobart cohort of Tasmania, Australia

BACKGROUND

The Greater Hobart region (42.5°S) of Tasmania has consistently had the highest recorded prevalence and incidence rates of multiple sclerosis (MS) in Australia. We reassessed MS epidemiology in 2009-2019 and assessed longitudinal changes over 68 years.

METHODS

Cases recruited from clinic-based datasets and multiple other data sources. 2019 prevalence and 2009-2019 annual incidence and mortality rates estimated, and differences assessed using Poisson regression.

RESULTS

436 MS cases resident on prevalence day were identified, and 130 had symptom onset within 2009-2019. Prevalence 197.1/100 000 (95% CI 179.4 to 216.5; 147.2/100 000 age standardised, 95% CI 126.5 to 171.3), a 36% increase since 2001 and 3.1-fold increase since 1961. 2009-2019 incidence rate=5.9/100 000 person-years, 95% CI 5.0 to 7.0 (6.1/1000 000 age standardised, 95% CI 4.7 to 7.9), a 2.8-fold increase since 1951-1961 and 65% since 2001-2009. 2009-2019 mortality rate=1.5/100 000 person-years, 95% CI 1.1 to 2.2 (0.9/100 000 age standardised, 95% CI 0.4 to 1.7), comparable to 2001-2009 (1.0/100 000) but reduced by 61% from 1951 to 1959 (2.1/100 000). 2001-2009 standardised mortality ratio=1.0 in 2009-2019, decreased from 2.0 in 1971-1979. Female:male prevalence sex ratio was 2.8, comparable to the 2009 value (2.6); incidence sex ratio (2.9) increased from 2001 to 9 (2.1). Comparisons with Newcastle, Australia (latitude=32.5°S) demonstrate a near complete abrogation of the latitudinal gradients for prevalence (ratio=1.0) and incidence (ratio=1.1), largely attributable to changing Hobart demography.

CONCLUSIONS

Prevalence and incidence of MS continue to increase significantly in Hobart, alongside marked reductions in mortality and increased case longevity. The marked increase in incidence is of particular note and may reflect longstanding changes in MS risk behaviours including changing sun exposure, obesity rates, and smoking behaviours, particularly in females. Falling mortality contributes to increase longevity and prevalence, likely reflecting improved overall MS healthcare and implementation of disease-modifying therapy.

Racial and Ethnic Disparities in Multiple Sclerosis Prevalence

BACKGROUND AND OBJECTIVES

The goal of this work was to determine whether the prevalence of multiple sclerosis (MS) varies by race and ethnicity.

METHODS

We conducted a retrospective cohort study of >2.6 million adults from the multiethnic, community-dwelling members of Kaiser Permanente Southern California. The complete electronic health records of individuals with at least 1 ICD-9 code for MS between January 1, 2008 and December 31, 2010 were reviewed. MS prevalence and 95% CIs stratified by age, sex, and race and ethnicity among 2010 members were estimated with binomial regression. Age- and sex-standardized prevalence was estimated according to the 2010 US Census population.

RESULTS

We identified 3,863 patients with MS. The average age of patients with prevalent MS was 51.7 years (SD 13.1 years), and 76.8% were women. The female preponderance was more pronounced among Black (81.2%) and Asian (83.6%) than White (76.3%) or Hispanic (74.5%) individuals with MS. Age- and sex-standardized MS prevalence per 100,000 was similarly high among Black (225.8, 95% CI 207.1-244.5) and White (237.7, 95% CI 228.2-247.2) and significantly lower among Hispanic (69.9, 95% CI 64.4-75.5) and Asian (22.6, 95% CI 17.1-28.1) persons. MS prevalence was highest between the ages of 35 and 64 years and declined after 65 years of age across all racial and ethnic groups. Among adults 18 to 24 years of age, the crude MS prevalence was low but was highest among Black and Hispanic young adults, lower in White people, and lowest in Asian/Pacific Islander individuals (48.5, 25.0, 18.0, and 7.1 per 100,000, respectively).

DISCUSSION

MS prevalence varies by race and ethnicity, being similarly high in White and Black and significantly lower in Hispanic and Asian persons in Southern California. Taken together with previous studies, these findings indicate that the burden of MS in the US Black community has long been underrecognized. More studies are needed to determine whether MS is an emerging disease among US Hispanic adults and whether MS susceptibility and prevalence vary among Hispanic or Asian individuals from different cultures or ancestral backgrounds.

Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes

Heterogeneity and limited comprehension of chronic autoimmune disease pathophysiology cause accurate diagnosis a challenging process. With the increasing resources of single-cell sequencing data, a reasonable way could be found to address this issue. In our study, with the use of large-scale public single-cell RNA sequencing (scRNA-seq) data, analysis of dataset integration (3.1 × 10⁵ PBMCs from fifteen SLE patients and eight healthy donors) and cellular cross talking (3.8 × 10⁵ PBMCs from twenty-eight SLE patients and eight healthy donors) were performed to identify the most crucial information characterizing SLE. Our findings revealed that the interactions among the PBMC subpopulations of SLE patients may be weakened under the inflammatory microenvironment, which could result in abnormal emergences or variations in signaling patterns within PBMCs. In particular, the alterations of B cells and monocytes may be the most significant findings. Utilizing this powerful information, an efficient mathematical model of unbiased random forest machine learning was established to distinguish SLE patients from healthy donors via not only scRNA-seq data but also bulk RNA-seq data. Surprisingly, our mathematical model could also accurately identify patients with rheumatoid arthritis and multiple sclerosis, not just SLE, via bulk RNA-seq data (derived from 688 samples). Since the variations in PBMCs should predate the clinical manifestations of these diseases, our machine learning model may be feasible to develop into an efficient tool for accurate diagnosis of chronic autoimmune diseases.