Intensity ratio to improve black hole assessment in multiple sclerosis

Mechanisms of central brain atrophy in multiple sclerosis

BACKGROUND

Change in ventricular volume has been suggested as surrogate measure of central brain atrophy (CBA) applicable to the everyday management of multiple sclerosis (MS) patients.

OBJECTIVES

We investigated the contribution of inflammatory activity (including the severity of lesional tissue damage) to CBA.

METHODS

Fifty patients with relapsing-remitting multiple sclerosis (RRMS) were enrolled. Lesional activity during 4 years of follow-up was analysed using custom-build software, which segmented expanding part of the chronic lesions, new confluent lesions and new free-standing lesions. The degree of lesional tissue damage was assessed by change in mean diffusivity (MD). Volumetric change of lateral ventricles was used to measure CBA.

RESULTS

During follow-up, ventricles expanded on average by 12.6% ± 13.7% (mean ± SD). There was a significant increase of total lesion volume, 69.3% of which was due to expansion of chronic lesions. Correlation between volume of combined lesional activity and CBA (r² = 0.67) increased when lesion volume was adjusted by the degree of tissue damage severity (r² = 0.81). Regression analysis explained 90% of CBA variability, revealing that chronic lesion expansion was by far the largest contributor to ventricular enlargement.

DISCUSSION

CBA is almost entirely explained by the combination of the volume and severity of lesional activity. The expansion of chronic lesions plays a central role in this process.

Evolution of acute "black hole" lesions in patients with relapsing-remitting multiple sclerosis

OBJECTIVE

Gadolinium-enhanced T1-weighted lesions are a well-established marker of areas with acute inflammatory activity. A majority of these gadolinium-enhanced T1 lesions are isointense relative to the surrounding white matter, but 20-40% of such active lesions will evolve during one year into areas of low signal ("black hole"). This study sought to characterize evolution of "black hole" lesions in patients with relapsing-remitting multiple sclerosis (MS) using the magnetic resonance imaging (MRI), which measures active lesions via the count of new or enlarged T2 and gadolinium-enhanced T1-weighted lesions.

MATERIALS AND METHODS

This was a prospective, observational case-series study which utilized pre- and post-gadolinium contrast T1-weighted and Proton density MRI scans. Twenty-nine patients (8 males and 21 females) with average age of 38.86 ± 6.58 years and disease duration of 5.75 ± 7.00 years were used to analyze 196 acute demyelinating plaques detected on MRI images during the 24-month follow-up of post-gadolinium signal intensity enhancement of MS plaques.

RESULTS

Significant difference in black hole development was found between the shapes of acute and chronic "black holes". Ring-shaped and patchy plaques were 4.09 (1.87-8.91) times more likely and 1.49 (0.71-3.12) times less likely to develop an acute "black holes" than homogeneous plaques, respectively. Acute plaques with higher lesion-to-CSF SI ratio and larger surface area showed a greater tendency to develop into acute and chronic "black holes".

CONCLUSIONS

The value of lesion-to-CSF SI ratio and surface area were found as the predictors of the "black hole" formation.

Quantitative magnetization transfer imaging in relapsing-remitting multiple sclerosis: a systematic review and meta-analysis

Myelin-sensitive MRI such as magnetization transfer imaging has been widely used in multiple sclerosis. The influence of methodology and differences in disease subtype on imaging findings is, however, not well established. Here, we systematically review magnetization transfer brain imaging findings in relapsing-remitting multiple sclerosis. We examine how methodological differences, disease effects and their interaction influence magnetization transfer imaging measures. Articles published before 06/01/2021 were retrieved from online databases (PubMed, EMBASE and Web of Science) with search terms including 'magnetization transfer' and 'brain' for systematic review, according to a pre-defined protocol. Only studies that used human in vivo quantitative magnetization transfer imaging in adults with relapsing-remitting multiple sclerosis (with or without healthy controls) were included. Additional data from relapsing-remitting multiple sclerosis subjects acquired in other studies comprising mixed disease subtypes were included in meta-analyses. Data including sample size, MRI acquisition protocol parameters, treatments and clinical findings were extracted and qualitatively synthesized. Where possible, effect sizes were calculated for meta-analyses to determine magnetization transfer (i) differences between patients and healthy controls; (ii) longitudinal change and (iii) relationships with clinical disability in relapsing-remitting multiple sclerosis. Eighty-six studies met inclusion criteria. MRI acquisition parameters varied widely, and were also underreported. The majority of studies examined the magnetization transfer ratio in white matter, but magnetization transfer metrics, brain regions examined and results were heterogeneous. The analysis demonstrated a risk of bias due to selective reporting and small sample sizes. The pooled random-effects meta-analysis across all brain compartments revealed magnetization transfer ratio was 1.17 per cent units (95% CI -1.42 to -0.91) lower in relapsing-remitting multiple sclerosis than healthy controls (z-value: -8.99, P < 0.001, 46 studies). Linear mixed-model analysis did not show a significant longitudinal change in magnetization transfer ratio across all brain regions [β = 0.12 (-0.56 to 0.80), t-value = 0.35, P = 0.724, 14 studies] or normal-appearing white matter alone [β = 0.037 (-0.14 to 0.22), t-value = 0.41, P = 0.68, eight studies]. There was a significant negative association between the magnetization transfer ratio and clinical disability, as assessed by the Expanded Disability Status Scale [r = -0.32 (95% CI -0.46 to -0.17); z-value = -4.33, P < 0.001, 13 studies]. Evidence suggests that magnetization transfer imaging metrics are sensitive to pathological brain changes in relapsing-remitting multiple sclerosis, although effect sizes were small in comparison to inter-study variability. Recommendations include: better harmonized magnetization transfer acquisition protocols with detailed methodological reporting standards; larger, well-phenotyped cohorts, including healthy controls; and, further exploration of techniques such as magnetization transfer saturation or inhomogeneous magnetization transfer ratio.

Multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis and neuropsychiatric systemic lupus erythematosus

OBJECTIVES

To develop an MRI-based multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis (RRMS) and its mimicker neuropsychiatric systemic lupus erythematosus (NPSLE).

METHODS

A total of 112 patients with RRMS (n = 63) or NPSLE (n = 49) were assigned to training and test sets with a ratio of 3:1. All lesions across the whole brain were manually segmented on T2-weighted fluid-attenuated inversion recovery images. For each single lesion, 371 radiomics features were extracted and trained using machine learning algorithms, producing Radiomics Index for Lesion (RIL) for each lesion and a single-lesion radiomics model. Then, for each subject, single lesions were assigned to one of two disease courts based on their distance to decision threshold, and a Radiomics Index for Subject (RIS) was calculated as the mean RIL value of lesions on the higher-weighted court. Accordingly, a subject-level discrimination model was constructed and compared with performances of two radiologists.

RESULTS

The subject-based discrimination model satisfactorily differentiated RRMS and NPSLE in both training (AUC = 0.967, accuracy = 0.892, sensitivity = 0.917, and specificity = 0.872) and test sets (AUC = 0.962, accuracy = 0.931, sensitivity = 1.000, and specificity = 0.875), significantly better than the single-lesion radiomics method (training: p < 0.001; test: p = 0.001) Besides, the discrimination model significantly outperformed the senior radiologist in the training set (training: p = 0.018; test: p = 0.077) and the junior radiologist in both the training and test sets (training: p = 0.008; test: p = 0.023).

CONCLUSIONS

The multi-lesion radiomics model could effectively discriminate between RRMS and NPSLE, providing a supplementary tool for accurate differential diagnosis of the two diseases.

KEY POINTS

• Radiomic features of brain lesions in RRMS and NPSLE were different. • The multi-lesion radiomics model constructed using a merging strategy was comprehensively superior to the single-lesion-based model for discrimination of RRMS and NPSLE. • The RRMS-NPSLE discrimination model showed a significantly better performance or a trend toward significance than the radiologists.

Correlation between the clinical disability and T1 hypointense lesions' volume in cerebral magnetic resonance imaging of multiple sclerosis patients: A systematic review and meta-analysis

BACKGROUND

To evaluate the correlation between T1 hypointense lesions' mean volume on cerebral MRI with disability level of patients with multiple sclerosis.

METHODS

We included studies testing the desired outcome in adult patients diagnosed with RRMS or SPMS. In Feb 2021, we searched PubMed, Embase, CENTRAL, and Web of Science to find relevant studies. All included studies were assessed for the risk of bias using a tailored version of the Quality in Prognosis Studies (QUIPS) tool. Extracted correlation coefficients were converted to the Fisher's z scale, and a meta-analysis using a random-effects model was performed on the results.

RESULTS

We included 27 studies (1919 participants). Meta-analysis revealed a correlation coefficient of 0.32 (95% CI 0.26-0.37) between T1 hypointense lesions' mean volume and EDSS score.

DISCUSSION

The correlation between T1 hypointense lesions' mean volume and EDSS was interpreted as low to slightly moderate. The certainty of the evidence was judged to be high.

To be, or not to be… Guillain-Barré Syndrome

Guillain-Barré Syndrome (GBS) is currently the most frequent cause of acute flaccid paralysis on a global scale, being an autoimmune disorder wherein demyelination of the peripheral nerves occurs. Its main clinical features are a symmetrical ascending muscle weakness with reduced osteotendinous reflexes and variable sensory involvement. GBS most commonly occurs after an infection, especially viral (including COVID-19), but may also transpire after immunization with certain vaccines or in the development of specific malignancies. Immunoglobulins, plasmapheresis, and glucocorticoids represent the principal treatment modalities, however patients with severe disease progression may require supportive therapy in an intensive care unit. Due to its symptomology, which overlaps with numerous neurological and infectious illnesses, the diagnosis of GBS may often be misattributed to pathologies that are essentially different from this syndrome. Moreover, many of these require specific treatment methods distinct to those recommended for GBS, in lack of which the prognosis of the patient is drastically affected. Such diseases include exposure to toxins either environmental or foodborne, central nervous system infections, metabolic or serum ion alterations, demyelinating pathologies, or even conditions amenable to neurosurgical intervention. This extensive narrative review aims to systematically and comprehensively tackle the most notable and challenging differential diagnoses of GBS, emphasizing on the clinical discrepancies between the diseases, the appropriate paraclinical investigations, and suitable management indications.

Two Classes of T1 Hypointense Lesions in Multiple Sclerosis With Different Clinical Relevance

Background: Hypointense lesions on T1-weighted images have important clinical relevance in multiple sclerosis patients. Traditionally, spin-echo (SE) sequences are used to assess these lesions (termed black holes), but Fast Spoiled Gradient-Echo (FSPGR) sequences provide an excellent alternative.

Objective: To determine whether the contrast difference between T1 hypointense lesions and the surrounding normal white matter is similar on the two sequences, whether different lesion types could be identified, and whether the clinical relevance of these lesions types are different.

Methods: Seventy-nine multiple sclerosis patients' lesions were manually segmented, then registered to T1 sequences. Median intensity values of lesions were identified on all sequences, then K-means clustering was applied to assess whether distinct clusters of lesions can be defined based on intensity values on SE, FSPGR, and FLAIR sequences. The standardized intensity of the lesions in each cluster was compared to the intensity of the normal appearing white matter in order to see if lesions stand out from the white matter on a given sequence.

Results: 100% of lesions on FSPGR images and 69% on SE sequence in cluster #1 exceeded a standardized lesion distance of Z = 2.3 (p < 0.05). In cluster #2, 78.7% of lesions on FSPGR and only 17.7% of lesions on SE sequence were above this cutoff value, meaning that these lesions were not easily seen on SE images. Lesion count in the second cluster (lesions less identifiable on SE) significantly correlated with the Expanded Disability Status Scale (EDSS) (R: 0.30, p ≤ 0.006) and with disease duration (R: 0.33, p ≤ 0.002).

Conclusion: We showed that black holes can be separated into two distinct clusters based on their intensity values on various sequences, only one of which is related to clinical parameters. This emphasizes the joint role of FSPGR and SE sequences in the monitoring of MS patients and provides insight into the role of black holes in MS.

Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions

OBJECTIVE

Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion-defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods.

METHODS

Thirty-eight MS patients were scanned with diffusion-weighted imaging, magnetization transfer imaging, and standard conventional MRI sequences (cMRI). A total of 499 regions of interest were identified on standard MRI and labeled as persistent black holes (PBH), persistent gray holes (PGH), acute black holes (ABH), acute gray holes (AGH), nonblack or gray holes (NBH), and normal appearing white matter (NAWM). DBSI, diffusion tensor imaging (DTI), and magnetization transfer ratio (MTR) were applied to the 43,261 imaging voxels extracted from these ROIs. The optimized DNN with 10 fully connected hidden layers was trained using the imaging metrics of the lesion subtypes and NAWM.

RESULTS

Concordance, sensitivity, specificity, and accuracy were determined for the different imaging methods. DBSI-DNN derived lesion classification achieved 93.4% overall concordance with predetermined lesion types, compared with 80.2% for DTI-DNN model, 78.3% for MTR-DNN model, and 74.2% for cMRI-DNN model. DBSI-DNN also produced the highest specificity, sensitivity, and accuracy.

CONCLUSIONS

DBSI-DNN improves the classification of different MS lesion subtypes, which could aid clinical decision making. The efficacy and efficiency of DBSI-DNN shows great promise for clinical applications in automatic MS lesion detection and classification.

Conversion of diffusely abnormal white matter to focal lesions is linked to progression in secondary progressive multiple sclerosis

Background:

Diffusely abnormal white matter (DAWM) regions are observed in magnetic resonance images of secondary progressive multiple sclerosis (SPMS) patients. However, their role in clinical progression is still not established.

Objectives:

To characterize the longitudinal volumetric and intensity evolution of DAWM and focal white matter lesions (FWML) and assess their associations with clinical outcomes and progression in SPMS.

Methods:

Data include 589 SPMS participants followed up for 3 years (3951 time points). FWML and DAWM were automatically segmented. Screening DAWM volumes that transformed into FWML at the last visit (DAWM-to-FWML) and normalized T1-weighted intensities (indicating severity of damage) in those voxels were calculated.

Results:

FWML volume increased and DAWM volume decreased with an increase in disease duration ( p < 0.001). The Expanded Disability Status Scale (EDSS) was positively associated with FWML volumes ( p = 0.002), but not with DAWM. DAWM-to-FWML volume was higher in patients who progressed (2.75 cm3 vs. 1.70 cm3; p < 0.0001). Normalized T1-weighted intensity of DAWM-to-FWML was negatively associated with progression ( p < 0.00001).

Conclusion:

DAWM transformed into FWML over time, and this transformation was associated with clinical progression. DAWM-to-FWML voxels had greater normalized T1-weighted intensity decrease over time, in keeping with relatively greater tissue damage. Evaluation of DAWM in progressive multiple sclerosis provides a useful measure for therapies aiming to protect this at-risk tissue with the potential to slow progression.