Background

A deep-learning prototype method, called CVSNet, was recently introduced for the automated detection of the central vein sign (CVS) in brain lesions and demonstrated effective and accurate discrimination of multiple sclerosis (MS) from its mimics. However, this method solely considered focal lesions displaying the central vein sign (CVS+) or not (CVS−), therefore requiring a manual pre-selection of the lesions to be evaluated by eliminating the so-called excluded lesions (CVSe) as defined by the NAIMS criteria. CVSe lesions may however play an important role in differential diagnosis. Moreover, extending the automated CVS classification to these lesions would facilitate the integration of CVSNet with existing MS lesion segmentation algorithms in a fully automated pipeline.

Objectives

To develop an improved version of the CVSNet prototype method able to classify all types of lesions (CVS+, CVS− and CVSe).

Methods

Patients with an established MS or CIS diagnosis (RRMS 29; SPMS 10; PPMS 10; CIS 1; mean ± SD age: 50 ± 11 years; male/female: 23/27), and healthy controls (n=8; mean ± SD age: 41 ± 9 years; male/female: 5/3), underwent 3T brain MRI (MAGNETOM Skyra and MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany, or Achieva, Philips Healthcare, Best, Netherlands). Brain lesions were automatically segmented and manually corrected by a single rater. CVS assessment was conducted on FLAIR* images by two raters, according to the NAIMS guidelines, yielding 1542 CVS+, 1004 CVS−, and 1131 CVSe lesions. A convolutional neural network (CNN) based on the CVSnet architecture was trained with different configurations using 3021 samples (1261 CVS+, 847 CVS−, and 913 CVSe) and evaluated in 656 unseen samples (281 CVS+, 157 CVS−, and 218 CVSe, from 13 patients) for final testing. The configurations relied on different combinations of the following channels as input: (i) FLAIR*, (ii) T2*, (iii) lesion mask, and (iv) CSF and brain tissue concentration maps obtained from a partial-volume estimation algorithm. Lesion-wise classification performance was evaluated for the different configurations by estimating the sensitivity, specificity, and accuracy for each lesion class.

Results

The results were similar across the different configurations. The best performance in the unseen testing set was obtained when all channels were used as input (sensitivity: 0.71, 0.73; specificity: 0.71, 0.81; and accuracy: 0.71, 0.79 for CVS+, CVS−, respectively). For CVSe, this approach achieved 0.52 sensitivity, 0.94 specificity, and 0.80 accuracy.

Conclusions

We introduced a modified CVSNet prototype method that can analyze the presence of the central vein for all types of brain lesions, enabling its integration with current MS lesion segmentation algorithms. This new feature will allow a fully automated assessment of the CVS in patients’ brains, speeding up the evaluation of CVS as a diagnostic biomarker for differentiating MS from mimicking diseases.

Background

Serum neurofilament light chain (sNfL) reflects neuro-axonal damage and may qualify as a biomarker of suboptimal response to disease modifying therapy (DMT).

Objectives

To investigate the predictive value of sNfL in clinically isolated syndrome (CIS) and relapsing-remitting (RR) MS patients with established DMT for future MS disease activity in the Swiss MS Cohort Study.

Methods

All patients were on DMT for at least 3 months. sNfL was measured 6 or 12-monthly with the NF-light^®assay. The association between sNfL and age was modeled using a generalized additive model for location scale and shape. Z-scores (sNfLz) were derived thereof, reflecting the deviation of a patient sNfL value from the mean value of same age healthy controls (n=8865 samples). We used univariable mixed logistic regression models to investigate the association between sNfLz and the occurrence of clinical events (relapses, EDSS worsening [≥1.5 steps if EDSS 0; ≥1.0 if 1.0-5.5 or ≥0.5 if >5.5] in the following year in all patients, and in those fulfilling NEDA-3 criteria (no relapses, EDSS worsening, contrast enhancing or new/enlarging T2 lesions in brain MRI, based on previous year). We combined sNfLz with clinical and MRI measures of MS disease activity in the previous year (EDA-3) in a multivariable mixed logistic regression model for predicting clinical events in the following year.

Results

sNfL was measured in 1062 patients with 5192 longitudinal samples (median age 39.7 yrs; EDSS 2.0; 4.1% CIS, 95.9% RRMS; median follow-up 5 yrs). sNfLz predicted clinical events in the following year (OR 1.21 [95%CI 1.11-1.36], p<0.001, n=4624). This effect increased in magnitude with increasing sNfLz (sNfLz >1: OR 1.41 [95%CI 1.15-1.73], p=0.001; >1.5: OR 1.80 [95%CI 1.43-2.28], p<0.001; >2: OR 2.33 [95%CI 1.74-3.14], p<0.001). Similar results were found for the prediction of future new/enlarging T2 lesions and brain volume loss. In the multivariable model, new/enlarging T2 lesions (OR 1.88 [95%CI 1.13-3.12], p=0.016) and sNfLz>1.5 (OR 2.18 [95%CI 1.21-3.90], p=0.009) predicted future clinical events (n=853), while previous EDSS worsening, previous relapses and current contrast enhancement did not. In NEDA-3 patients, change of sNfLz (per standard deviation) was associated with a 37% increased risk of clinical events in the subsequent year (OR 1.37 [95%CI 1.04-1.78], p=0.025, n=587).

Conclusions

Our data support the value of sNfL levels, beyond the NEDA3 concept, for treatment monitoring in MS clinical practice.

Background

In multiple sclerosis (MS), perilesional chronic inflammation appears on in vivo 3T susceptibility-based magnetic resonance imaging (MRI) as non-gadolinium-enhancing paramagnetic rim lesions (PRL). A higher PRL burden has been recently associated with a more aggressive disease course. The visual detection of PRL by experts is time-consuming and can be subjective.

Objectives

To develop a multimodal convolutional neural network (CNN) capable of automatically detecting PRL on 3D-T2*w-EPI unwrapped phase and 3D-T2w-FLAIR images.

Methods

124 MS cases (87 relapsing remitting MS, 16 primary progressive MS and 21 secondary progressive MS) underwent 3T MRI (MAGNETOM Prisma and MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany). Two neurologists visually inspected FLAIR magnitude and EPI phase images and annotated 462 PRL. 4857 lesions detected by an automatic segmentation (La Rosa et al. 2019) without overlap with PRL were considered non-PRL. The prototype RimNet was built upon two single CNNs, each fed with 3D patches centered on candidate lesions in phase and FLAIR images, respectively. A two-step feature-map fusion, initially after the first convolutional block and then before the fully connected layers, enhances the extraction of low and high-level multimodal features. For comparison, two unimodal CNNs were trained with phase and FLAIR images. The areas under the ROC curve (AUC) were used for evaluation (DeLong et al. 1988). The operating point was set at a lesion-wise specificity of 0.95. The patient-wise assessment was conducted by using a clinically relevant threshold of four rim+ lesions per patient (Absinta et al. 2019).

Results

RimNet (AUC=0.943) outperformed the phase and FLAIR image unimodal networks (AUC=0.913 and 0.855, respectively, P’s <0.0001). At the operating point, RimNet showed higher lesion-wise sensitivity (70.6%) than the unimodal phase network (62.1%), but lower than the experts (77.7%). At the patient level, RimNet performed with sensitivity of 86.8% and specificity of 90.7%. Individual expert ratings yielded averaged sensitivity and specificity values of 76.3% and 99.4%, respectively.

Conclusions

The excellent performance of RimNet supports its further development as an assessment tool to automatically detect PRL in MS. Interestingly, the unimodal FLAIR network performed reasonably well despite the absence of a paramagnetic rim, suggesting that morphometric features such as volume or shape might be a distinguishable feature of PRL.

Background

Intrathecal IgM synthesis was reported to be associated with higher clinical disease activity and severity. We found an association also with earlier use of high efficacy treatments in relapsing MS (RMS).

Objectives

To explore whether patients with intrathecal IgM synthesis show a) higher serum neurofilament light chain levels (sNfL) as a reflection of neuronal damage, or b) signs of increased disease severity in cerebral MRI, in patients with RMS followed in the Swiss MS Cohort Study.

Methods

487 patients were categorized by presence of oligoclonal IgG bands (OCGB) and intrathecally produced IgG/M:

1) OCGB-/IgG-/IgM- (reference [ref]);

2) OCGB+/IgG-/IgM-;

3) OCGB+/IgG+/IgM- and

4) OCGB+/IgG+/IgM+.

sNfL was measured (at baseline and every 6- or 12 months) with the NF-light^® assay. Age-dependent sNfL z-scores (sNfLz) were modelled in 8865 healthy control samples to reflect the deviation of a patient sNfL value compared to mean values observed in same age healthy controls. Yearly T2 lesion number and occurrence of new/enlarging T2 lesions were automatically assessed in cerebral MRIs and checked manually. Contrast enhancing lesions (CEL) were manually quantified. Linear or negative binomial mixed models were used to investigate the associations between the four CSF Ig patterns and longitudinal sNfLz and MRI measures, adjusted for DMT and other covariates.

Results

IgM+ patients had higher sNfLz vs reference (estimate 0.50 [CI 0.12, 0.89], p=0.011), whereas those with only OCGB+ (0.11 [-0.28, 0.50], p=0.582) or with OCGB+/IgG+ (0.20 [-0.16, 0.56], p=0.270) did not (n=2970 observations). This was confirmed when analyzing only untreated patients adjusting for T2 and CEL numbers (1.16 [0.47, 1.86], p<0.01 vs 0.58 [-0.11, 1.27], p=0.1022 vs 0.51 [-0.11, 1.13], p=0.108 vs ref, respectively) (n=234).

IgM+ patients had 2.28-fold more T2 lesions ([1.51, 3.44], p<0.01) vs ref; for patients with only OCGB+ (1.61 [1.07, 2.43], p=0.0237) or OCGB+/IgG+ (1.58 [CI 1.08, 2.32], p=0.0179) (n=1580) this association was weaker.

IgM+ was associated with a 2.47-fold risk for new/enlarging T2 lesions on yearly follow-up MRIs vs ref (2.47 [1.28, 4.78], p<0.01) but not the two other patient groups (1.84 [CI 0.93; 3.65], p=0.0799 and 1.61 [CI 0.87; 2.95], p=0.1280) (n=861).

Conclusions

Intrathecal IgM synthesis was consistently associated with quantitative measures of neuro-axonal injury and disease severity in RMS. Our findings strongly support the clinical utiliy of this biomarker.

Background

In relapsing MS, blood NfL has emerged as a promising biomarker of disease activity and worsening. The ability of serum NfL (sNfL) to detect relapse-independent disability progression is less well established.

Objectives

We investigated whether patients followed in the Swiss Multiple Sclerosis Cohort (SMSC) without any relapses during follow-up, had higher sNfL levels when experiencing confirmed disability progression independent of relapses (PIRA) as compared to stable patients. Secondly, we explored whether baseline (BL) sNfL could predict PIRA.

Methods

BL and 6- or 12-monthly follow-up sNfL were measured by Simoa NF-light^® assay in 4608 samples from 806 relapse-free MS patients and 8865 serum samples from 4133 healthy controls (median age 45 yrs). Age-dependent sNfL z-scores (sNfLz) were modeled in healthy controls using a generalised additive model for location scale and shape to reflect the deviation of a patient sNfL value from the mean value of same age healthy controls. PIRA was defined as an EDSS increase of ≥1.5 steps if baseline EDSS 0, ≥1.0 if 1.0-5.5, or ≥0.5 if >5.5, confirmed after ≥6 months. We used mixed effects models to investigate the association between PIRA, clinical parameters, disease modifying treatment, and log(sNfL) as dependent variable at each sampling. The predictive value of BL sNfLz was investigated by uni- and multivariable Cox proportional hazards models.

Results

806 (4608 samples) of 1399 patients in the SMSC did not experience relapses during a median follow-up of 4.7 years (57.6%; BL: 715 RRMS, 43 SPMS, 48 PPMS; median age 42 yrs; samples/patient: 5; EDSS 2.0). PIRA occurred in 153/806 (19.0%). In a multivariable model, sNfL was positively associated with age (1.7%/year [95%CI 1.5;2.0], p<0.001) and EDSS at BL (7.6%/step, [5.8;9.6], p<0.001), whereas it was decreased when sampled during monoclonal antibody therapy (-10.8%, [-14.7;-6.6], p<0.001) or oral MS treatments (-10.4%, [-14.1;-6.5%], p<0.001) as compared to untreated timepoints. Importantly, patients experiencing PIRA had 11.6% higher sNfL levels, compared with stable patients (4.5;19.2, p=0.001). The hazard of future PIRA increased by 23.5% (8.3;40.8, p=0.002) per 1 standard deviation higher BL sNfLz. This finding was confirmed after adjusting for age, EDSS score and treatment at BL (27.8%, [11.5;46.5], p<0.001; sNfLz > 2: 2.5-fold risk [95%CI 1.7-3.9], p<0.001 for PIRA event vs. sNfLz < 2).

Conclusions

Our data support the value of sNfL to capture and predict neuro-axonal injury leading to disability progression independent from relapses.

Background

Serum neurofilament light chain (sNfL) levels reflect neuroaxonal damage and relate to disease activity in MS. sNfL may qualify as well as a biomarker of suboptimal treatment response to disease modifying therapies (DMT). Establishment of age-dependent reference ranges in healthy controls is a prerequisite for developing this biomarker for clinical use.

Objectives

To compare on-treatment sNfL levels with values from a healthy control cohort and to investigate the effect of DMTs on sNfL levels in patients from the Swiss MS Cohort Study.

Methods

sNfL was measured (at baseline and every 6- or 12 months) with the NF-light® assay. Age-dependent sNfL z-scores (sNfLz) were modeled in healthy controls using a generalized additive model for location scale and shape to reflect the deviation of a patient sNfL value from the mean value of same age healthy controls. Linear mixed models were used to investigate the associations between clinical characteristics, DMT and longitudinal sNfLz. Interaction terms and splines were used to model sNfLz and for comparison log(NfL), and their dynamics under treatment.

Results

sNfL was measured in 1368 patients with 7550 longitudinal samples (baseline: median age: 41.9 yrs; 5.4% CIS, 83.2% RRMS, 5.6% SPMS, 5.8% PPMS; median EDSS: 2.0; median follow-up: 4.6 yrs) and 4133 healthy controls with 8865 samples (median age: 44.8 yrs). In the multivariable model, sNfLz increased with EDSS (0.131/step, [95% CI 0.101;0.161]), recent (<120 days) relapse (0.739 [0.643;0.835]) decreased with age (-0.014/year [-0.02;-0.009]), and time on DMT (-0.040/year [-0.054;-0.027]); sNfLz were lower when sampled while on more effective DMT (oral versus platform injectables: -0.229 [-0.344;-0.144]; monoclonal antibodies (mAB) versus platform injectables: -0.349 [-0.475;-0.224]), (p<0.001 for all associations). sNfLz were inversely associated with the hierarchy in efficacy of mAB over orals and orals over platform therapies with regard to slope and extent of decrease (interaction between time under DMT and DMT class: p<0.001). sNfLz, but not log(NfL) showed normalization of sNfL levels by mAB to healthy control levels.

Conclusions

The dynamic change of sNfLz on DMT reflects closely their relative clinical efficacy and is more meaningful than log(sNfL) by excluding age as a confounding factor. Use of sNfLz based on a large normative database as an age-independent sNfL measure improves the accuracy of the sNfL signal and hence their clinical utility.

Background

In multiple sclerosis (MS), a subset of chronic active white matter lesions are identifiable on MRI by their paramagnetic rims, and increasing evidence supports their association with clinical disease severity.

Objectives

To assess the prevalence and MS-specificity of paramagnetic rim lesions (PRL) on 3-tesla susceptibility-based MR brain images in MS vs non-MS cases in a multicenter sample drawn from 5 academic research hospitals at sites in Europe (Brussels, Lausanne, Milan) and the United States (NIH and JHU).

Methods

On submillimetric 3D T2*-segmented EPI brain MRI, the presence of PRL and central vein sign (CVS) were evaluated in the supratentorial brain of adults with MS (n=329) and non-MS neurological conditions (n=83). Non-MS cases were grouped as follows: (1) other-inflammatory neurological diseases (n=41); (2) HTLV-associated myelopathy/tropical spastic paraparesis (HAM/TSP; n=10); (3) HIV-infected (n=10); (4) non-inflammatory neurological diseases (n=22).

ROC curve analysis, with diagnosis as dependent variable (MS vs non-MS), was applied to examine the diagnostic accuracy for each biomarker (PRL and CVS). Youden’s index method was used to obtain the optimal cutoff value for each biomarker.

Results

PRL were detected in 172/329 (52%) of MS cases vs. 6/83 non-MS cases (7%).

In MS, 58% of progressive cases had at least one PRL, compared to 50% of relapsing cases. MS cases with more than 4 PRL were more likely to have higher disability scores (EDSS, MSSS and ARMSS), but not significantly longer disease duration or older age.

In non-MS cases, PRL were seen exclusively in only a few inflammatory/infectious neurological conditions, including Susac syndrome (3 cases), neuromyelitis optica spectrum disorder (1 case), Sjögren disease (1 case) and HAM/TSP (1 case). Unlike in MS, PRL in non-MS cases were not associated with a high frequency of CVS+ lesions.

The identification of at least one PRL (optimal cutoff) was associated with high diagnostic specificity (93%), but relatively low sensitivity (52%) and accuracy (area under ROC curve=0.77), whereas CVS detection alone (optimal cutoff 35.5-38%) could better discriminate MS from non-MS cases with high specificity (96%), sensitivity (99%), and accuracy (area under ROC curve=0.99). The combination of the two biomarkers further improved the specificity (99%), but sensitivity remained low (59%).

Conclusions

PRL yielded high specificity for MS lesions. Future prospective multicenter studies should further validate its role as a diagnostic biomarker.

Background

Advanced diffusion-weighted MRI (DW-MRI) sequences, in combination with biophysical models, provide new information on the microstructural properties of the tissue.

Objectives

To investigate the differences in intra-axonal signal fraction (IASF) between perilesional normal-appearing white matter (pl-NAWM), white matter lesions (WML) without (rim-) and with paramagnetic rim (rim+) comparing eight biophysical diffusion models.

Methods

The study included 102 MS patients: RRMS: 66%, SPMS: 18%, PPMS: 16%, mean age 46±14; female 64%, disease duration 12.16±18.18 yrs, median EDSS: 2.5.

DW-MRI data were acquired with 1.8mm isotropic resolution and b-values [0, 700, 1000, 2000, 3000] s/mm².

Lesion masks were generated with a deep-learning-based method and manually corrected if required; pl-NAWM was defined as a region of 3-voxels around each WML; 225 paramagnetic rim lesions were manually identified based on 3D EPI and 2330 were labelled as rim-.

The following microstructural models were applied: Ball and Stick, Ball and Rockets, AMICO-NODDI, SMT-NODDI, MCMDI, NODDIDA, CHARMED, Microstructure Bayesian approach.

Delta (WML - pl-NAWM) was calculated for each WML, and one-side Mann Whitney U was used to compare the delta between models, followed by Bonferroni to correct for multiple testing.

Mean difference and Cohen's d was used to assess differences between lesions with extensive axonal damage (rim+) and other WML (rim-).

Results

All models applied in this study reported low IASF in rim+ WML, medium IASF in rim- WML and relatively high IASF in pl-NAWM. However, a broad spectrum of IASF values was identified from the different models: relatively simple models such as Ball and Stick and CHARMED, showed low delta IASF within lesions, while MCMDI models reported the highest significant difference compared to other models (p<0.0001). The comparison between WML and pl-NAWM mean IASF across models showed that MCDMI exhibited the highest difference (mean 0.13, Cohen’s d 1.34). AMICO-NODDI and SMT-NODDI showed close results (mean difference 0.12/0.12 and Cohen’s d 1.46/1.51).

The models best discriminating IASF between rim+ and rim- lesions were MCMDI and NODIDDA (mean 0.08/0.07, Cohen’s d -0.69/-0.70).

Conclusions

We compared eight WM diffusion models for assessment of intralesional axonal damage in MS patients. The comparison between WML and pl-NAWM showed that robustness of the method, identified with SMT-based and NODDI-based models, it is crucial. For the comparison between lesions with a high level of damage (rim +) and other WML, the diffusivity estimation appeared to play an important role. The method which appeared both robust and able to estimate the diffusivity of the tissue was MCMDI, which performed best in both cases.