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

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.

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.