Temporal association of sNfL and gad-enhancing lesions in multiple sclerosis.
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ABSTRACT:
SUBMITTER: Rosso M
PROVIDER: S-EPMC7318095 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesIn multiple sclerosis (MS), contrast-enhancing lesions (CELs) in T1-weighted postcontrast MRI are considered markers of blood-brain barrier breakdown. It remains unknown if re-enhancement can be considered a radiologic indicator of different pathology in CELs. We investigated 1) the incidence of re-enhancing lesions (re-CELs) from chronic lesions; 2) differences in size, magnetization transfer ratio (MTR), and likelihood to appear as acute black holes (aBHs) between new lesions (n-CELs) and re-CELs; and 3) associations between re-CELs and features indicating more advanced disease.MethodsIn this retrospective natural history study, we examined 264 monthly MRI scans performed at month 1 (M1), month 2 (M2), and month 3 (M3) for 88 patients with MS. CELs were defined as n-CELs if not present in the M1 T2W MRI and re-CELs if present in the M1 T2W MRI.ResultsA total of 311 (82.7%) n-CELs and 65 (17.3%) re-CELs were identified. Of the 88 patients, 54 presented only n-CELs, 8 presented only re-CELs, and 26 presented both CEL types. Patients with both lesion types presented more CELs than those presenting only one type (p = 0.01). Re-CELs were larger (z = 2.72, p = 0.007) and had lower MTR (z = -2.80, p = 0.005) than n-CELs but the estimated proportion of aBHs from n-CELs was similar (z = -0.09, p = 0.1) from the proportion of aBHs from re-CELs.ConclusionsNearly 20% of CELs represent the reoccurrence of enhancement in chronic plaques. Re-CELs represent larger areas of inflammation, not necessarily associated with larger areas of edema.
| S-EPMC3345616 | biostudies-literature
Project description:ObjectiveThe aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients.MethodsA three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing-remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume.ResultsThe DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm3) were 0.81/0.97/0.04. For U2, the average DSC/TPR/FPR values were 0.72/0.86/0.31. Comparable performance was observed with U1. For all types of input, the network performance degraded with decreased enhancement size.ConclusionExcellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm3. The best performance was achieved when the input included all five multispectral image sets.
| S-EPMC7680286 | biostudies-literature
Project description:Background Enhancing lesions on MRI scans obtained after contrast material administration are commonly thought to represent disease activity in multiple sclerosis (MS); it is desirable to develop methods that can predict enhancing lesions without the use of contrast material. Purpose To evaluate whether deep learning can predict enhancing lesions on MRI scans obtained without the use of contrast material. Materials and Methods This study involved prospective analysis of existing MRI data. A convolutional neural network was used for classification of enhancing lesions on unenhanced MRI scans. This classification was performed for each slice, and the slice scores were combined by using a fully connected network to produce participant-wise predictions. The network input consisted of 1970 multiparametric MRI scans from 1008 patients recruited from 2005 to 2009. Enhanced lesions on postcontrast T1-weighted images served as the ground truth. The network performance was assessed by using fivefold cross-validation. Statistical analysis of the network performance included calculation of lesion detection rates and areas under the receiver operating characteristic curve (AUCs). Results MRI scans from 1008 participants (mean age, 37.7 years ± 9.7; 730 women) were analyzed. At least one enhancing lesion was observed in 519 participants. The sensitivity and specificity averaged across the five test sets were 78% ± 4.3 and 73% ± 2.7, respectively, for slice-wise prediction. The corresponding participant-wise values were 72% ± 9.0 and 70% ± 6.3. The diagnostic performances (AUCs) were 0.82 ± 0.02 and 0.75 ± 0.03 for slice-wise and participant-wise enhancement prediction, respectively. Conclusion Deep learning used with conventional MRI identified enhanced lesions in multiple sclerosis from images from unenhanced multiparametric MRI with moderate to high accuracy. © RSNA, 2019.
| S-EPMC6980901 | biostudies-literature
Project description:PurposeIn multiple sclerosis (MS) the sensitivity for detection of contrast enhancing lesions (CEL) in T1-weighted scans is essential for diagnostics and therapy decisions. The purpose of our study was to evaluate the sensitivity of T1w MPRAGE scans in comparison to T1w dark blood technique (T1-DB) for CEL in MS.Materials and methods3T MR imaging was performed in 37 MS patients, including T2-weighted imaging, T1w MPRAGE before and after gadolinium injection (unenhanced-T1 and T1-CE) and T1-DB imaging. After gadolinium application, the T1-DB scan was performed prior to T1-CE. From unenhanced-T1 and T1-CE scans, subtraction images (T1-SUB) were calculated. The number of CEL was determined separately on T1-CE and T1-DB by two raters independently. Lesions only detected on T1-DB scans then were verified on T1-SUB. Only lesions detected by both raters were included in further analysis.ResultsIn 16 patients, at least one CEL was detected by both rater, either on T1-CE or T1-DB. All lesions that were detected on T1-CE were also detected on T1-DB images. The total number of contrast enhancing lesions detected on T1-DB images (n = 54) by both raters was significantly higher than the corresponding number of lesions identified on T1-CE (n = 27) (p = 0.01); all of these lesions could be verified on SUB images. In 21 patients, no CEL was detected in any of the sequences.ConclusionsThe application of T1-DB technique increases the sensitivity for CEL in MS, especially for those lesions that show only subtle increase in intensity after Gadolinium application but remain hypo- or iso-intense to surrounding tissue.
| S-EPMC5552307 | biostudies-literature
Project description:BackgroundContrast-enhancing (CE) lesions are an important finding on brain magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) but can be missed easily. Automated solutions for reliable CE lesion detection are emerging; however, independent validation of artificial intelligence (AI) tools in the clinical routine is still rare.MethodsA three-dimensional convolutional neural network for CE lesion segmentation was trained externally on 1488 datasets of 934 MS patients from 81 scanners using concatenated information from FLAIR and T1-weighted post-contrast imaging. This externally trained model was tested on an independent dataset comprising 504 T1-weighted post-contrast and FLAIR image datasets of MS patients from clinical routine. Two neuroradiologists (R1, R2) labeled CE lesions for gold standard definition in the clinical test dataset. The algorithmic output was evaluated on both patient- and lesion-level.ResultsOn a patient-level, recall, specificity, precision, and accuracy of the AI tool to predict patients with CE lesions were 0.75, 0.99, 0.91, and 0.96. The agreement between the AI tool and both readers was within the range of inter-rater agreement (Cohen's kappa; AI vs. R1: 0.69; AI vs. R2: 0.76; R1 vs. R2: 0.76). On a lesion-level, false negative lesions were predominately found in infratentorial location, significantly smaller, and at lower contrast than true positive lesions (p < 0.05).ConclusionsAI-based identification of CE lesions on brain MRI is feasible, approaching human reader performance in independent clinical data and might be of help as a second reader in the neuroradiological assessment of active inflammation in MS patients.Critical relevance statementAl-based detection of contrast-enhancing multiple sclerosis lesions approaches human reader performance, but careful visual inspection is still needed, especially for infratentorial, small and low-contrast lesions.
| S-EPMC10350445 | biostudies-literature
Project description:ImportanceIn multiple sclerosis (MS), chronic active lesions, which previously could only be detected at autopsy, can now be identified on susceptibility-based magnetic resonance imaging (MRI) in vivo as non-gadolinium-enhancing lesions with paramagnetic rims. Pathologically, they feature smoldering inflammatory demyelination at the edge, remyelination failure, and axonal degeneration. To our knowledge, the prospect of long-term in vivo monitoring makes it possible for the first time to determine their contribution to disability and value as a treatment target.ObjectiveTo assess whether rim lesions are associated with patient disability and long-term lesion outcomes.Design, setting, participantsWe performed 3 studies at the National Institutes of Health Clinical Center: (1) a prospective clinical/radiological cohort of 209 patients with MS (diagnosis according to the 2010 McDonald revised MS criteria, age ≥18 years, with 7-T or 3-T susceptibility-based brain MRI results) who were enrolled from January 2012 to March 2018 (of 209, 17 patients [8%] were excluded because of uninterpretable MRI scans); (2) a radiological/pathological analysis of expanding lesions featuring rims; and (3) a retrospective longitudinal radiological study assessing long-term lesion evolution in 23 patients with MS with yearly MRI scans for 10 years or more (earliest scan, 1992).Main outcomes and measures(1) Identification of chronic rim lesions on 7-T or 3-T susceptibility-based brain MRI in 192 patients with MS and the association of rim counts with clinical disability (primary analysis) and brain volume changes (exploratory analysis). (2) Pathological characterization of 10 expanding lesions from an adult with progressive MS who came to autopsy after 7 years of receiving serial in vivo MRI scans. (3) Evaluation of annual lesion volume change (primary analysis) and T1 times (exploratory analysis) in 27 rim lesions vs 27 rimless lesions.ResultsOf 209 participants, 104 (50%) were women and 32 (15%) were African American. One hundred seventeen patients (56%) had at least 1 rim lesion regardless of prior or ongoing treatment. Further, 84 patients (40%) had no rims (mean [SD] age, 47 [14] years), 66 (32%) had 1 to 3 rims (mean [SD] age, 47 [11] years), and 42 (20%) had 4 rims or more (mean [SD] age, 44 [11] years). Individuals with 4 rim lesions or more reached motor and cognitive disability at an earlier age. Normalized volumes of brain, white matter, and basal ganglia were lower in those with rim lesions. Whereas rimless lesions shrank over time (-3.6%/year), rim lesions were stable in size or expanded (2.2%/year; P < .001). Rim lesions had longer T1 times, suggesting more tissue destruction, than rimless lesions. On histopathological analysis, all 10 rim lesions that expanded in vivo had chronic active inflammation.Conclusions and relevanceChronic active lesions are common, are associated with more aggressive disease, exert ongoing tissue damage, and occur even in individuals treated with effective disease-modifying therapies. These results prompt the planning of MRI-based clinical trials aimed at treating perilesional chronic inflammation in MS.
| S-EPMC6692692 | biostudies-literature
Project description:Gadolinium-enhancing lesions reflect active disease and are critical for in-patient monitoring in multiple sclerosis (MS). In this work, we have developed the first fully automated method to segment and count the gadolinium-enhancing lesions from routine clinical MRI of MS patients. The proposed method first segments the potential lesions using 2D-UNet from multi-channel scans (T1 post-contrast, T1 pre-contrast, FLAIR, T2, and proton-density) and classifies the lesions using a random forest classifier. The algorithm was trained and validated on 600 MRIs with manual segmentation. We compared the effect of loss functions (Dice, cross entropy, and bootstrapping cross entropy) and number of input contrasts. We compared the lesion counts with those by radiologists using 2,846 images. Dice, lesion-wise sensitivity, and false discovery rate with full 5 contrasts were 0.698, 0.844, and 0.307, which improved to 0.767, 0.969, and 0.00 in large lesions (>100 voxels). The model using bootstrapping loss function provided a statistically significant increase of 7.1% in sensitivity and of 2.3% in Dice compared with the model using cross entropy loss. T1 post/pre-contrast and FLAIR were the most important contrasts. For large lesions, the 2D-UNet model trained using T1 pre-contrast, FLAIR, T2, PD had a lesion-wise sensitivity of 0.688 and false discovery rate 0.083, even without T1 post-contrast. For counting lesions in 2846 routine MRI images, the model with 2D-UNet and random forest, which was trained with bootstrapping cross entropy, achieved accuracy of 87.7% using T1 pre-contrast, T1 post-contrast, and FLAIR when lesion counts were categorized as 0, 1, and 2 or more. The model performs well in routine non-standardized MRI datasets, allows large-scale analysis of clinical datasets, and may have clinical applications.
| S-EPMC8409666 | biostudies-literature
Project description:ObjectivesThe disruption of the blood-brain barrier (BBB) is a key and early feature in the pathogenesis of demyelinating multiple sclerosis (MS) lesions and has been neuropathologically demonstrated in both active and chronic plaques. The local overt BBB disruption in acute demyelinating lesions is captured as signal hyperintensity in post-contrast T1-weighted images because of the contrast-related shortening of the T1 relaxation time. On the contrary, the subtle BBB disruption in chronic lesions is not visible at conventional radiological evaluation but it might be of clinical relevance. Indeed, persistent, subtle BBB leakage might be linked to low-grade inflammation and plaque evolution. Here we hypothesised that 3D Quantitative Transient-state Imaging (QTI) was able to reveal and measure T1 shortening (ΔT1) reflecting small amounts of contrast media leakage in apparently non-enhancing lesions (ANELs).Materials and methodsThirty-four patients with relapsing remitting MS were included in the study. All patients underwent a 3 T MRI exam of the brain including conventional sequences and QTI acquisitions (1.1 mm isotropic voxel) performed both before and after contrast media administration. For each patient, a ΔT1 map was obtained via voxel-wise subtraction of pre- and post- contrast QTI-derived T1 maps. ΔT1 values measured in ANELs were compared with those recorded in enhancing lesions and in the normal appearing white matter. A reference distribution of ΔT1 in the white matter was obtained from datasets acquired in 10 non-MS patients with unrevealing MR imaging.ResultsMean ΔT1 in ANELs (57.45 ± 48.27 ms) was significantly lower than in enhancing lesions (297.71 ± 177.52 ms; p < 0. 0001) and higher than in the normal appearing white matter (36.57 ± 10.53 ms; p < 0.005). Fifty-two percent of ANELs exhibited ΔT1 higher than those observed in the white matter of non-MS patients.ConclusionsQTI-derived quantitative ΔT1 mapping enabled to measure contrast-related T1 shortening in ANELs. ANELs exhibiting ΔT1 values that deviate from the reference distribution in non-MS patients may indicate persistent, subtle, BBB disruption. Access to this information may be proved useful to better characterise pathology and objectively monitor disease activity and response to therapy.
| S-EPMC10514220 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2016-07-03 | E-GEOD-60943 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below.
2016-01-01 | GSE60943 | GEO