Dataset Information

Characterizing contrast-enhancing and re-enhancing lesions in multiple sclerosis.

ABSTRACT:

Objectives

In 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.

Methods

In 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.

Results

A 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.

Conclusions

Nearly 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.

SUBMITTER: Campbell Z

PROVIDER: S-EPMC3345616 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Publications

Characterizing contrast-enhancing and re-enhancing lesions in multiple sclerosis.

Campbell Z Z Sahm D D Donohue K K Jamison J J Davis M M Pellicano C C Auh S S Ohayon J J Frank J A JA Richert N N Bagnato F F

Neurology 20120425 19

<h4>Objectives</h4>In 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 lesion ...[more]

PMID: 22539575

Similar Datasets

Project description:BackgroundMultiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are the two mimic autoimmune diseases of the central nervous system, which are rare in East Asia. Quantitative detection of contrast-enhancing lesions (CELs) on contrast-enhancing T1-weighted magnetic resonance (MR) images is of great significance for assessing the disease activity of MS and NMOSD. However, it is challenging to develop automatic segmentation algorithms due to the lack of data. In this work, we present an automatic segmentation model of CELs based on Fully Convolutional with Attention DenseNet (FCA-DenseNet) and transfer learning strategy to address the challenge of CEL quantification in small-scale datasets.MethodsA transfer learning approach was employed in this study, whereby pretraining was conducted using 77 MS subjects from the open access datasets (MICCAI 2016, MICCAI 2017, ISBI 2015) for white matter hyperintensity segmentation, followed by fine-tuning using 24 MS and NMOSD subjects from the local dataset for CEL segmentation. The proposed FCA-DenseNet combined the Fully Convolutional DenseNet and Convolutional Block Attention Module in order to improve the learning capability. A 2.5D data slicing strategy was used to process complex 3D MR images. U-Net, ResUNet, TransUNet, and Attention-UNet are used as comparison models to FCA-DenseNet. Dice similarity coefficient (DSC), positive predictive value (PPV), true positive rate (TPR), and volume difference (VD) are used as evaluation metrics to evaluate the performances of different models.ResultsFCA-DenseNet outperforms all other models in terms of all evaluation metrics, with a DSC of 0.661±0.187, PPV of 0.719±0.201, TPR of 0.680±0.254, and VD of 0.388±0.334. Transfer learning strategy has achieved success in building segmentation models on a small-scale local dataset where traditional deep learning approaches fail to train effectively.ConclusionsThe improved FCA-DenseNet, combined with transfer learning strategy and 2.5D data slicing strategy, has successfully addressed the challenges in constructing deep learning models on small-scale datasets, making it conducive to clinical quantification of brain CELs and diagnosis of MS and NMOSD.

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.

Dataset Information

Characterizing contrast-enhancing and re-enhancing lesions in multiple sclerosis.

Objectives

Methods

Results

Conclusions

Publications

Characterizing contrast-enhancing and re-enhancing lesions in multiple sclerosis.

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