Exploring individual multiple sclerosis lesion volume change over time: Development of an algorithm for the analyses of longitudinal quantitative MRI measures

An automated methodology for whole-body, multimodality tracking of individual cancer lesions

Objective. Manual analysis of individual cancer lesions to assess disease response is clinically impractical and requires automated lesion tracking methodologies. However, no methodology has been developed for whole-body individual lesion tracking, across an arbitrary number of scans, and acquired with various imaging modalities.Approach. This study introduces a lesion tracking methodology and benchmarked it using 2368Ga-DOTATATE PET/CT and PET/MR images of eight neuroendocrine tumor patients. The methodology consists of six steps: (1) alignment of multiple scans via image registration, (2) body-part labeling, (3) automatic lesion-wise dilation, (4) clustering of lesions based on local lesion shape metrics, (5) assignment of lesion tracks, and (6) output of a lesion graph. Registration performance was evaluated via landmark distance, lesion matching accuracy was evaluated between each image pair, and lesion tracking accuracy was evaluated via identical track ratio. Sensitivity studies were performed to evaluate the impact of lesion dilation (fixed versus automatic dilation), anatomic location, image modalities (inter- versus intra-modality), registration mode (direct versus indirect registration), and track size (number of time-points and lesions) on lesion matching and tracking performance.Main results. Manual contouring yielded 956 lesions, 1570 lesion-matching decisions, and 493 lesion tracks. The median residual registration error was 2.5 mm. The automatic lesion dilation led to 0.90 overall lesion matching accuracy, and an 88% identical track ratio. The methodology is robust regarding anatomic locations, image modalities, and registration modes. The number of scans had a moderate negative impact on the identical track ratio (94% for 2 scans, 91% for 3 scans, and 81% for 4 scans). The number of lesions substantially impacted the identical track ratio (93% for 2 nodes versus 54% for ≥5 nodes).Significance. The developed methodology resulted in high lesion-matching accuracy and enables automated lesion tracking in PET/CT and PET/MR.

3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies

Background and purpose

Deep learning algorithms for segmentation of multiple sclerosis (MS) plaques generally require training on large datasets. This manuscript evaluates the effect of transfer learning from segmentation of another pathology to facilitate use of smaller MS-specific training datasets. That is, a model trained for detection of one type of pathology was re-trained to identify MS lesions and active demyelination.

Materials and methods

In this retrospective study using MRI exams from 149 patients spanning 4/18/2014 to 7/8/2021, 3D convolutional neural networks were trained with a variable number of manually-segmented MS studies. Models were trained for FLAIR lesion segmentation at a single timepoint, new FLAIR lesion segmentation comparing two timepoints, and enhancing (actively demyelinating) lesion segmentation on T1 post-contrast imaging. Models were trained either de-novo or fine-tuned with transfer learning applied to a pre-existing model initially trained on non-MS data. Performance was evaluated with lesionwise sensitivity and positive predictive value (PPV).

Results

For single timepoint FLAIR lesion segmentation with 10 training studies, a fine-tuned model demonstrated improved performance [lesionwise sensitivity 0.55 ± 0.02 (mean ± standard error), PPV 0.66 ± 0.02] compared to a de-novo model (sensitivity 0.49 ± 0.02, p = 0.001; PPV 0.32 ± 0.02, p < 0.001). For new lesion segmentation with 30 training studies and their prior comparisons, a fine-tuned model demonstrated similar sensitivity (0.49 ± 0.05) and significantly improved PPV (0.60 ± 0.05) compared to a de-novo model (sensitivity 0.51 ± 0.04, p = 0.437; PPV 0.43 ± 0.04, p = 0.002). For enhancement segmentation with 20 training studies, a fine-tuned model demonstrated significantly improved overall performance (sensitivity 0.74 ± 0.06, PPV 0.69 ± 0.05) compared to a de-novo model (sensitivity 0.44 ± 0.09, p = 0.001; PPV 0.37 ± 0.05, p = 0.001).

Conclusion

By fine-tuning models trained for other disease pathologies with MS-specific data, competitive models identifying existing MS plaques, new MS plaques, and active demyelination can be built with substantially smaller datasets than would otherwise be required to train new models.

Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.

A Multicenter Longitudinal MRI Study Assessing LeMan-PV Software Accuracy in the Detection of White Matter Lesions in Multiple Sclerosis Patients

BACKGROUND

Detecting new and enlarged lesions in multiple sclerosis (MS) patients is needed to determine their disease activity. LeMan-PV is a software embedded in the scanner reconstruction system of one vendor, which automatically assesses new and enlarged white matter lesions (NELs) in the follow-up of MS patients; however, multicenter validation studies are lacking.

PURPOSE

To assess the accuracy of LeMan-PV for the longitudinal detection NEL white-matter MS lesions in a multicenter clinical setting.

STUDY TYPE

Retrospective, longitudinal.

SUBJECTS

A total of 206 patients with a definitive MS diagnosis and at least two follow-up MRI studies from five centers participating in the Swiss Multiple Sclerosis Cohort study. Mean age at first follow-up = 45.2 years (range: 36.9-52.8 years); 70 males.

FIELD STRENGTH/SEQUENCE

Fluid attenuated inversion recovery (FLAIR) and T1-weighted magnetization prepared rapid gradient echo (T1-MPRAGE) sequences at 1.5 T and 3 T.

ASSESSMENT

The study included 313 MRI pairs of datasets. Data were analyzed with LeMan-PV and compared with a manual "reference standard" provided by a neuroradiologist. A second rater (neurologist) performed the same analysis in a subset of MRI pairs to evaluate the rating-accuracy. The Sensitivity (Se), Specificity (Sp), Accuracy (Acc), F1-score, lesion-wise False-Positive-Rate (aFPR), and other measures were used to assess LeMan-PV performance for the detection of NEL at 1.5 T and 3 T. The performance was also evaluated in the subgroup of 123 MRI pairs at 3 T.

STATISTICAL TESTS

Intraclass correlation coefficient (ICC) and Cohen's kappa (CK) were used to evaluate the agreement between readers.

RESULTS

The interreader agreement was high for detecting new lesions (ICC = 0.97, Pvalue < 10-20 , CK = 0.82, P value = 0) and good (ICC = 0.75, P value < 10-12 , CK = 0.68, P value = 0) for detecting enlarged lesions. Across all centers, scanner field strengths (1.5 T, 3 T), and for NEL, LeMan-PV achieved: Acc = 61%, Se = 65%, Sp = 60%, F1-score = 0.44, aFPR = 1.31. When both follow-ups were acquired at 3 T, LeMan-PV accuracy was higher (Acc = 66%, Se = 66%, Sp = 66%, F1-score = 0.28, aFPR = 3.03).

DATA CONCLUSION

In this multicenter study using clinical data settings acquired at 1.5 T and 3 T, and variations in MRI protocols, LeMan-PV showed similar sensitivity in detecting NEL with respect to other recent 3 T multicentric studies based on neural networks. While LeMan-PV performance is not optimal, its main advantage is that it provides automated clinical decision support integrated into the radiological-routine flow.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Chronic demyelination of rabbit lesions is attributable to failed oligodendrocyte progenitor cell repopulation

The failure of remyelination in the human CNS contributes to axonal injury and disease progression in multiple sclerosis (MS). In contrast to regions of chronic demyelination in the human brain, remyelination in murine models is preceded by abundant oligodendrocyte progenitor cell (OPC) repopulation, such that OPC density within regions of demyelination far exceeds that of normal white matter (NWM). As such, we hypothesized that efficient OPC repopulation was a prerequisite of successful remyelination, and that increased lesion volume may contribute to the failure of OPC repopulation in human brain. In this study, we characterized the pattern of OPC activation and proliferation following induction of lysolecithin-induced chronic demyelination in adult rabbits. The density of OPCs never exceeded that of NWM and oligodendrocyte density did not recover even at 6 months post-injection. Rabbit OPC recruitment in large lesions was further characterized by chronic Sox2 expression in OPCs located in the lesion core and upregulation of quiescence-associated Prrx1 mRNA at the lesion border. Surprisingly, when small rabbit lesions of equivalent size to mouse were induced, they too exhibited reduced OPC repopulation. However, small lesions were distinct from large lesions as they displayed an almost complete lack of OPC proliferation following demyelination. These differences in the response to demyelination suggest that both volume dependent and species-specific mechanisms are critical in the regulation of OPC proliferation and lesion repopulation and suggest that alternate models will be necessary to fully understand the mechanisms that contribute to failed remyelination in MS.

Improving the detection of new lesions in multiple sclerosis with a cascaded 3D fully convolutional neural network approach

Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new lesions on brain MRI scans is considered a robust predictive biomarker for the disease progression. New lesions are a high-impact prognostic factor to predict evolution to MS or risk of disability accumulation over time. However, the detection of this disease activity is performed visually by comparing the follow-up and baseline scans. Due to the presence of small lesions, misregistration, and high inter-/intra-observer variability, this detection of new lesions is prone to errors. In this direction, one of the last Medical Image Computing and Computer Assisted Intervention (MICCAI) challenges was dealing with this automatic new lesion quantification. The MSSEG-2: MS new lesions segmentation challenge offers an evaluation framework for this new lesion segmentation task with a large database (100 patients, each with two-time points) compiled from the OFSEP (Observatoire français de la sclérose en plaques) cohort, the French MS registry, including 3D T2-w fluid-attenuated inversion recovery (T2-FLAIR) images from different centers and scanners. Apart from a change in centers, MRI scanners, and acquisition protocols, there are more challenges that hinder the automated detection process of new lesions such as the need for large annotated datasets, which may be not easily available, or the fact that new lesions are small areas producing a class imbalance problem that could bias trained models toward the non-lesion class. In this article, we present a novel automated method for new lesion detection of MS patient images. Our approach is based on a cascade of two 3D patch-wise fully convolutional neural networks (FCNNs). The first FCNN is trained to be more sensitive revealing possible candidate new lesion voxels, while the second FCNN is trained to reduce the number of misclassified voxels coming from the first network. 3D T2-FLAIR images from the two-time points were pre-processed and linearly co-registered. Afterward, a fully CNN, where its inputs were only the baseline and follow-up images, was trained to detect new MS lesions. Our approach obtained a mean segmentation dice similarity coefficient of 0.42 with a detection F1-score of 0.5. Compared to the challenge participants, we obtained one of the highest precision scores (PPVL = 0.52), the best PPVL rate (0.53), and a lesion detection sensitivity (SensL of 0.53).

Assessment of T2 lesion-based disease activity volume outcomes in predicting disease progression in multiple sclerosis over 10 years

BACKGROUND

New/enlarging T2 lesion count and T2-lesion volume (LV) are used as conventional secondary endpoints in clinical trials of patients with multiple sclerosis (PwMS). However, those outcomes may have several limitations, such as inability to account for heterogeneity of lesion formation/enlargement frequency and their dynamic volumetric behavior. Measurement of volume rather than count of new/enlarging lesions may be more representative outcome of dynamic changes over time.

OBJECTIVES

To investigate whether new/enlarging T2-LV is more predictive of confirmed disability progression (CDP), compared to total T2-LV or new/enlarging T2 lesion count over long-term follow-up.

METHODS

We studied 176 early relapsing-remitting PwMS who were followed with annual MRI examinations over 10 years. T2-LV, new/enlarging T2-LV, and new/enlarging lesion count were determined. Cumulative count/volumes were obtained. 10-year CDP was confirmed after 48-weeks. ANCOVA analysis detected MRI outcome differences in stable (n = 76) and CDP (n = 100) groups at different time points, after correction for multiple comparisons.

RESULTS

PwMS with CDP had greater cumulative new/enlarging T2-LV at 4 years (p = 0.049), and enlarging T2-LV at 4- (p = 0.039) and 6-year follow-up (p = 0.032), compared to stable patients. PwMS with CDP did not differ from stable ones in new/enlarging T2 lesion count or total T2-LV at any of the study timepoints. PwMS with Expanded Disability Status Scale change >2.0 had significantly greater enlarging T2 lesion count (p = 0.01) and enlarging T2-LV (p = 0.038) over the 10-year follow-up.

CONCLUSION

Enlargement of T2 lesions is more strongly associated with long-term disability progression compared to other conventional T2 lesion-based outcomes.

Exploring in vivo lesion myelination dynamics: Longitudinal Myelin Water Imaging in early Multiple Sclerosis

BACKGROUND

Multiple Sclerosis (MS) lesions are pathologically heterogeneous and the temporal behavior in terms of growth and myelination status of individual lesions is highly variable, especially in the early phase of the disease. Thus, monitoring the development of individual lesion myelination by using quantitative magnetic resonance myelin water imaging (MWI) could be valuable to capture the variability of disease pathology and get an individual insight into the subclinical disease activity.

OBJECTIVE

The goal of this work was (1) to observe the variation and longitudinal change of in vivo lesion myelination by means of MWI and its parameter Myelin Water Fraction (MWF), and, (2) to identify individual lesion myelination patterns in early MS.

METHODS

In this study n = 12 patients obtained conventional MRI and quantitative MWI derived from multi-component driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) within four weeks after presenting a clinically isolated syndrome and remained within the study if clinically definitive MS was diagnosed within the 12 months study period. Four MRI sessions were acquired at baseline, 3, 6, and 12 months. The short-term and long-term variability of MWF maps was evaluated by scan-rescan measures and the coefficient of variation was determined in four healthy controls. Tracking of individual lesions was performed using the Automatic Follow-up of Individual Lesions (AFIL) algorithm. Lesion volume and MWF were evaluated for every individual lesion in all patients. Median lesion MWF change was used to define lesion categories as decreasing, varying, increasing and invariant for MWF variation.

RESULTS

In total n = 386 T2 lesions were detected with a subset of n = 225 permanent lesions present at all four time-points. Among those, a heterogeneous lesion MWF reduction was found, with the majority of lesions bearing only mild MWF reduction, approximately a third with an intermediate MWF decrease and highest MWF reduction in acute-inflammatory active lesions. A moderate negative correlation was determined between individual lesion volumes and median MWF consistent across all time-points. Permanent lesions featured variable temporal dynamics with the majority of varying MWF (58 %), however decreasing (16 %), increasing (15 %) and invariant (11 %) subgroups could be identified resembling demyelinating activity and post-demyelinating inactivity known from histopathology studies. Inflammatory-active enhancing lesions showed a distinct pattern of MWF reduction followed by partial recovery after 3 months. This was similar in new enhancing lesions and those with a non-enhancing precursor lesion.

CONCLUSION

This work provides in vivo evidence for an individual evolution of early demyelinated MS lesions measured by means of MWF imaging. Our results support the hypothesis, that MS lesions undergo multiple demyelination and remyelination episodes in the early acute phase. The in vivo MRI surrogate of myelin turnover bears capacity as a novel biomarker to select and potentially monitor personalized MS treatment.

Image registration and appearance adaptation in non-correspondent image regions for new MS lesions detection

Manual detection of newly formed lesions in multiple sclerosis is an important but tedious and difficult task. Several approaches for automating the detection of new lesions have recently been proposed, but they tend to either overestimate the actual amount of new lesions or to miss many lesions. In this paper, an image registration convolutional neural network (CNN) that adapts the baseline image to the follow-up image by spatial deformations and simulation of new lesions is proposed. Simultaneously, segmentations of new lesions are generated, which are shown to reliably estimate the real new lesion load and to separate stable and progressive patients. Several applications of the proposed network emerge: image registration, detection and segmentation of new lesions, and modeling of new MS lesions. The modeled lesions offer the possibility to investigate the intensity profile of new lesions.

New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images

Multiple sclerosis (MS) is an autoimmune disease that causes lesions in the central nervous system of humans due to demyelinating axons. Magnetic resonance imaging (MRI) is widely used for monitoring and measuring MS lesions. Automated methods for MS lesion segmentation have usually been performed on individual MRI scans. Recently, tracking lesion activity for quantifying and monitoring MS disease progression, especially detecting new lesions, has become an important biomarker. In this study, a unique pipeline with a deep neural network that combines U-Net, attention gate, and residual learning is proposed to perform better new MS lesion segmentation using baseline and follow-up 3D FLAIR MR images. The proposed network has a similar architecture to U-Net and is formed from residual units which facilitate the training of deep networks. Networks with fewer parameters are designed with better performance through the skip connections of U-Net and residual units, which facilitate information propagation without degradation. Attention gates also learn to focus on salient features of the target structures of various sizes and shapes. The MSSEG-2 dataset was used for training and testing the proposed pipeline, and the results were compared with those of other proposed pipelines of the challenge and experts who participated in the same challenge. According to the results over the testing set, the lesion-wise F1 and dice scores were obtained as a mean of 48 and 44.30%. For the no-lesion cases, the number of tested and volume of tested lesions were obtained as a mean of 0.148 and 1.488, respectively. The proposed pipeline outperformed 22 proposed pipelines and ranked 8th in the challenge.

Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions

Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system that affects nearly 1 million adults in the United States. Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis and treatment monitoring in MS patients. In particular, follow-up MRI with T2-FLAIR images of the brain, depicting white matter lesions, is the mainstay for monitoring disease activity and making treatment decisions. In this article, we present a computational approach that has been deployed and integrated into a real-world routine clinical workflow, focusing on two tasks: (a) detecting new disease activity in MS patients, and (b) determining the necessity for injecting Gadolinium Based Contract Agents (GBCAs). This computer-aided detection (CAD) software has been utilized for the former task on more than 19, 000 patients over the course of 10 years, while its added function of identifying patients who need GBCA injection, has been operative for the past 3 years, with > 85% sensitivity. The benefits of this approach are summarized in: (1) offering a reproducible and accurate clinical assessment of MS lesion patients, (2) reducing the adverse effects of GBCAs (and the deposition of GBCAs to the patient's brain) by identifying the patients who may benefit from injection, and (3) reducing healthcare costs, patients' discomfort, and caregivers' workload.

Dynamic topology analysis for spatial patterns of multifocal lesions on MRI

Quantitatively analysing the spatial patterns of multifocal lesions on clinical MRI is an important step towards a better understanding of the disease and for precision medicine, which is yet to be properly explored by feature engineering and deep learning methods. Network science addresses this issue by explicitly modeling the inter-lesion topology. However, the construction of the informative graph with optimal edge sparsity and quantification of community graph structures are the current challenges in network science. In this paper, we address these challenges with a novel Dynamic Topology Analysis framework on the basis of persistent homology, aiming to investigate the predictive values of global geometry and local clusters of multifocal lesions. Firstly, Dynamic Hierarchical Network is proposed to construct informative global and community-level topology over multi-scale networks from sparse to dense. Multi-scale global topology is constructed with a nested sequence of Rips complexes, from which a new K-simplex Filtration is designed to generate a higher-level topological abstraction for community identification based on the connectivity of k-simplices in the Rips Complex. Secondly, to quantify multi-scale community structures, we design a new Decomposed Community Persistence algorithm to track the dynamic evolution of communities, and then summarise the evolutionary communities incorporated with a customisable descriptor. The quantified community features are encapsulated with global geometric invariants for topological pattern analysis. The proposed framework was evaluated on both diagnostic differentiation and prognostic prediction for multiple sclerosis that is a typical multifocal disease, and achieved ROC_AUC 0.875 and 0.767, respectively, outperforming seven state-of-the-art persistent homology methods and the reported performance of six feature engineering and deep learning methods.

Development and validation of a longitudinal soft-tissue metastatic lesion matching algorithm

Metastatic cancer presents with many, sometimes hundreds of metastatic lesions through the body, which often respond heterogeneously to treatment. Therefore, lesion-level assessment is necessary for a complete understanding of disease response. Lesion-level assessment typically requires manual matching of corresponding lesions, which is a tedious, subjective, and error-prone task. This study introduces a fully automated algorithm for matching of metastatic lesions in longitudinal medical images. The algorithm entails four steps: (1) image registration, (2) lesion dilation, (3) lesion clustering, and (4) linear assignment. In step (1), 3D deformable registration is used to register the scans. In step (2), lesion contours are conformally dilated. In step (3), lesion clustering is evaluated based on local metrics. In step (4), matching is assigned based on non-greedy cost minimization. The algorithm was optimized (e.g. choice of deformable registration algorithm, dilatation size) and validated on 140 scan-pairs of 32 metastatic cancer patients from two independent clinical trials, who received longitudinal PET/CT scans as part of their treatment response assessment. Registration error was evaluated using landmark distance. A sensitivity study was performed to evaluate the optimal lesion dilation magnitude. Lesion matching performance accuracy was evaluated for all patients and for a subset with high disease burden. Two investigated deformable registration approaches (whole body deformable and articulated deformable registrations) led to similar performance with the overall registration accuracy between 2.3 and 2.6 mm. The optimal dilation magnitude of 25 mm yielded almost a perfect matching accuracy of 0.98. No significant matching accuracy decrease was observed in the subset of patients with high lesion disease burden. In summary, lesion matching using our new algorithm was highly accurate and a significant improvement, when compared to previously established methods. The proposed method enables accurate automated metastatic lesion matching in whole-body longitudinal scans.

Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks

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A fully automated segmentation of new or enlarged multiple sclerosis (MS) lesions.

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3D convolutional neural network (CNN) with U-net-like encoder-decoder architecture.

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Simultaneous processing of baseline and follow-up scan of the same patient.

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Trained on 3253 patient data from over 103 different MR scanners.

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Fast (<1min), robust algorithm with segmentation results in inter-rater variability.

The quantification of new or enlarged lesions from follow-up MRI scans is an important surrogate of clinical disease activity in patients with multiple sclerosis (MS). Not only is manual segmentation time consuming, but inter-rater variability is high. Currently, only a few fully automated methods are available. We address this gap in the field by employing a 3D convolutional neural network (CNN) with encoder-decoder architecture for fully automatic longitudinal lesion segmentation.

Input data consist of two fluid attenuated inversion recovery (FLAIR) images (baseline and follow-up) per patient. Each image is entered into the encoder and the feature maps are concatenated and then fed into the decoder. The output is a 3D mask indicating new or enlarged lesions (compared to the baseline scan). The proposed method was trained on 1809 single point and 1444 longitudinal patient data sets and then validated on 185 independent longitudinal data sets from two different scanners. From the two validation data sets, manual segmentations were available from three experienced raters, respectively. The performance of the proposed method was compared to the open source Lesion Segmentation Toolbox (LST), which is a current state-of-art longitudinal lesion segmentation method.

The mean lesion-wise inter-rater sensitivity was 62%, while the mean inter-rater number of false positive (FP) findings was 0.41 lesions per case. The two validated algorithms showed a mean sensitivity of 60% (CNN), 46% (LST) and a mean FP of 0.48 (CNN), 1.86 (LST) per case. Sensitivity and number of FP were not significantly different (p < 0.05) between the CNN and manual raters.

New or enlarged lesions counted by the CNN algorithm appeared to be comparable with manual expert ratings. The proposed algorithm seems to outperform currently available approaches, particularly LST. The high inter-rater variability in case of manual segmentation indicates the complexity of identifying new or enlarged lesions. An automated CNN-based approach can quickly provide an independent and deterministic assessment of new or enlarged lesions from baseline to follow-up scans with acceptable reliability.

New MRI lesions and topography at 6 months of treatment initiation and disease activity during follow up in relapsing remitting multiple sclerosis patients

Objective: The objective of this study was to assess if the presence of new lesions and their topography on the reference MRI have a prognostic value regarding disease activity during the follow up in relapsing remitting multiple sclerosis (RRMS) patients.Methods: Retrospective cohort study that included patients with RRMS who had a reference MRI (performed at 6 months from the onset of a DMT) and radiological and clinical follow up for at least two years. We identified the number of new MRI lesions and their topography at reference MRI and during the follow up. Cox proportional hazards model analysis was used to evaluate the association between new lesions on reference MRI and the appearance of new lesions and/or clinical relapses at 24-month follow-up.Results: 56 patients were included, 13 (23.2%) showed new lesions in the reference MRI. The presence of new lesions at reference MRI predicted the occurrence of new lesions at month 24 (HR 3.1, CI 95% 2.5-5.8). The number of lesions and the infratentorial topography at reference MRI were associated with an increased risk of new radiological activity during follow up (HR 3.5, IC95% 3.1-6.1 and HR 2.4, IC95% 1.9-2.7 respectively).Conclusion: New lesions at the reference MRI in terms of number and topography increase the risk of radiological disease activity during the follow up.

A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis

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A deep learning model for new T2-w lesions detection in multiple sclerosis is presented.

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Combining a learning-based registration network with a segmentation one increases the performance.

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The proposed model decreases false-positives while increasing true-positives.

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Better performance compared to other supervised and unsupervised state-of-the-art approaches.

Introduction: Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w lesions on brain MR scans is considered a predictive biomarker for the disease. In this study, we propose a fully convolutional neural network (FCNN) to detect new T2-w lesions in longitudinal brain MR images.

Methods: One year apart, multichannel brain MR scans (T1-w, T2-w, PD-w, and FLAIR) were obtained for 60 patients, 36 of them with new T2-w lesions. Modalities from both temporal points were preprocessed and linearly coregistered. Afterwards, an FCNN, whose inputs were from the baseline and follow-up images, was trained to detect new MS lesions. The first part of the network consisted of U-Net blocks that learned the deformation fields (DFs) and nonlinearly registered the baseline image to the follow-up image for each input modality. The learned DFs together with the baseline and follow-up images were then fed to the second part, another U-Net that performed the final detection and segmentation of new T2-w lesions. The model was trained end-to-end, simultaneously learning both the DFs and the new T2-w lesions, using a combined loss function. We evaluated the performance of the model following a leave-one-out cross-validation scheme.

Results: In terms of the detection of new lesions, we obtained a mean Dice similarity coefficient of 0.83 with a true positive rate of 83.09% and a false positive detection rate of 9.36%. In terms of segmentation, we obtained a mean Dice similarity coefficient of 0.55. The performance of our model was significantly better compared to the state-of-the-art methods (p < 0.05).

Conclusions: Our proposal shows the benefits of combining a learning-based registration network with a segmentation network. Compared to other methods, the proposed model decreases the number of false positives. During testing, the proposed model operates faster than the other two state-of-the-art methods based on the DF obtained by Demons.

Longitudinal analysis of white matter and cortical lesions in multiple sclerosis

PURPOSE

The goals of this study were to assess the performance of a novel lesion segmentation tool for longitudinal analyses, as well as to validate the generated lesion progression map between two time points using conventional and non-conventional MR sequences.

MATERIAL AND METHODS

The lesion segmentation approach was evaluated with (LeMan-PV) and without (LeMan) the partial volume framework using "conventional" and "non-conventional" MR imaging in a two-year follow-up prospective study of 32 early RRMS patients. Manual segmentations of new, enlarged, shrunken, and stable lesions were used to evaluate the performance of the method variants. The true positive rate was estimated for those lesion evolutions in both white matter and cortex. The number of false positives was compared with two strategies for longitudinal analyses. New lesion tissue volume estimation was evaluated using Bland-Altman plots. Wilcoxon signed-rank test was used to evaluate the different setups.

RESULTS

The best median of the true positive rate was obtained using LeMan-PV with non-conventional sequences (P < .05): 87%, 87%, 100%, 83%, for new, enlarged, shrunken, and stable WM lesions, and 50%, 60%, 50%, 80%, for new, enlarged, shrunken, and stable cortical lesions, respectively. Most of the missed lesions were below the mean lesion size in each category. Lesion progression maps presented a median of 0 false positives (range:0-9) and the partial volume framework improved the volume estimation of new lesion tissue.

CONCLUSION

LeMan-PV exhibited the best performance in the detection of new, enlarged, shrunken and stable WM lesions. The method showed lower performance in the detection of cortical lesions, likely due to their low occurrence, small size and low contrast with respect to surrounding tissues. The proposed lesion progression map might be useful in clinical trials or clinical routine.

Development of Cortical Lesion Volumes on Double Inversion Recovery MRI in Patients With Relapse-Onset Multiple Sclerosis

Background and Objective: In multiple sclerosis (MS) patients, Double Inversion Recovery (DIR) magnetic resonance imaging (MRI) can be used to detect cortical lesions (CL). While the quantity and distribution of CLs seems to be associated with patients' disease course, literature lacks frequent assessments of CL volumes (CL-V) in this context. We investigated the reliability of DIR for the longitudinal assessment of CL-V development with frequent follow-up MRIs and examined the course of CL-V progressions in relation to white-matter lesions (WML), contrast enhancing lesions (CEL) and clinical parameters in patients with Relapsing-Remitting Multiple Sclerosis (RRMS).

Methods: In this post-hoc analysis, image- and clinical data of a subset of 24 subjects that were part of a phase IIa clinical trial on the “Safety, Tolerability and Mechanisms of Action of Boswellic Acids in Multiple Sclerosis (SABA)” (ClinicalTrials.gov, NCT01450124) were included. The study was divided in three phases (screening, treatment, study-end). All patients received 12 MRI follow-up-examinations (including DIR) during a 16-months period. CL-Vs were assessed for each patient on each follow-up MRI separately by two experienced neuroradiologists. Results of neurological screening tests, as well as other MRI parameters (WML number and volume and CELs) were included from the SABA investigation data.

Results: Inter-rater agreement regarding CL-V assessment over time was good-to-excellent (κ = 0.89). Mean intraobserver variability was 1.1%. In all patients, a total number of 218 CLs was found. Total CL-Vs of all patients increased during the 4 months of baseline screening followed by a continuous and significant decrease from month 5 until study-end (p < 0.001, Kendall'W = 0.413). A positive association between WML volumes and CL-Vs was observed during baseline screening. Decreased CL-V were associated with lower EDSS and also with improvements of SDMT- and SCRIPPS scores.

Conclusion: DIR MRI seems to be a reliable tool for the frequent assessment of CL-Vs. Overall CL-Vs decreased during the follow-up period and were associated with improvements of cognitive and disability status scores. Our results suggest the presence of short-term CL-V dynamics in RRMS patients and we presume that the laborious evaluation of lesion volumes may be worthwhile for future investigations.

Clinical Trial Numbers:www.ClinicalTrials.gov, “The SABA trial”; number: NCT01450124