Current and Emerging Therapies in Multiple Sclerosis: Implications for the Radiologist, Part 1-Mechanisms, Efficacy, and Safety

A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS

BACKGROUND AND PURPOSE

MS is a chronic progressive, idiopathic, demyelinating disorder whose diagnosis is contingent on the interpretation of MR imaging. New MR imaging lesions are an early biomarker of disease progression. We aimed to evaluate a machine learning model based on radiomics features in predicting progression on MR imaging of the brain in individuals with MS.

MATERIALS AND METHODS

This retrospective cohort study with external validation on open-access data obtained full ethics approval. Longitudinal MR imaging data for patients with MS were collected and processed for machine learning. Radiomics features were extracted at the future location of a new lesion in the patients' prior MR imaging ("prelesion"). Additionally, "control" samples were obtained from the normal-appearing white matter for each participant. Machine learning models for binary classification were trained and tested and then evaluated the external data of the model.

RESULTS

The total number of participants was 167. Of the 147 in the training/test set, 102 were women and 45 were men. The average age was 42 (range, 21-74 years). The best-performing radiomics-based model was XGBoost, with accuracy, precision, recall, and F1-score of 0.91, 0.91, 0.91, and 0.91 on the test set, and 0.74, 0.74, 0.74, and 0.70 on the external validation set. The 5 most important radiomics features to the XGBoost model were associated with the overall heterogeneity and low gray-level emphasis of the segmented regions. Probability maps were produced to illustrate potential future clinical applications.

CONCLUSIONS

Our machine learning model based on radiomics features successfully differentiated prelesions from normal-appearing white matter. This outcome suggests that radiomics features from normal-appearing white matter could serve as an imaging biomarker for progression of MS on MR imaging.

Contrast-Enhanced 3D Spin Echo T1-Weighted Sequence Outperforms 3D Gradient Echo T1-Weighted Sequence for the Detection of Multiple Sclerosis Lesions on 3.0 T Brain MRI

BACKGROUND

Using reliable contrast-enhanced T1 sequences is crucial to detect enhancing brain lesions for multiple sclerosis (MS) at the time of diagnosis and over follow-up. Contrast-enhanced 3D gradient-recalled echo (GRE) T1-weighted imaging (WI) and 3D turbo spin echo (TSE) T1-WI are both available for clinical practice and have never been compared within the context of this diagnosis.

PURPOSE

The aim of this study was to compare contrast-enhanced 3D GRE T1-WI and 3D TSE T1-WI for the detection of enhancing lesions in the brains of MS patients.

METHODS

This single-center prospective study enrolled patients with MS who underwent a 3.0 T brain MRI from August 2017 to April 2021 for follow-up. Contrast-enhanced 3D GRE T1-WI and 3D TSE T1-WI were acquired in randomized order. Two independent radiologists blinded to all data reported all contrast-enhanced lesions in each sequence. Their readings were compared with a reference standard established by a third expert neuroradiologist. Interobserver agreement, contrast ratio, and contrast-to-noise ratio were calculated for both sequences.

RESULTS

A total of 158 MS patients were included (mean age, 40 ± 11 years; 95 women). Significantly more patients had at least 1 contrast-enhanced lesion on 3D TSE T1-WI than on 3D GRE T1-WI for both readers (61/158 [38.6%] vs 48/158 [30.4%] and 60/158 [38.6%] vs 47/158 [29.7%], P < 0.001). Significantly more contrast-enhanced lesions per patient were detected on 3D TSE T1-WI (mean 2.47 vs 1.56 and 2.56 vs 1.39, respectively, P < 0.001). Interobserver agreement was excellent for both sequences, κ = 0.96 (confidence interval [CI], 0.91-1.00) for 3D TSE T1-WI and 0.92 (CI, 0.86-0.99) for 3D GRE T1-WI. Contrast ratio and contrast-to-noise ratio were significantly higher on 3D TSE T1-WI (0.84 vs 0.53, P < 0.001, and 87.9 vs 57.8, P = 0.03, respectively).

CONCLUSIONS

At 3.0 T, contrast-enhanced 3D TSE-T1-WI supports the detection of significantly more enhancing lesions than 3D GRE T1-WI and should therefore be used for MS patients requiring contrast-enhanced examination.

The ethical matrix as a method for involving people living with disease and the wider public (PPI) in near-term artificial intelligence research

INTRODUCTION

The rapid pace of research in the field of Artificial Intelligence in medicine has associated risks for near-term AI. Ethical considerations of the use of AI in medicine remain a subject of much debate. Concurrently, the Involvement of People living with disease and the Public (PPI) in research is becoming mandatory in the EU and UK. The goal of this research was to elucidate the important values for our relevant stakeholders: People with MS, Radiologists, neurologists, Registered Healthcare Practitioners and Computer Scientists concerning AI in radiology and synthesize these in an ethical matrix.

METHODS

An ethical matrix workshop co-designed with a patient expert. The workshop yielded a survey which was disseminated to the professional societies of the relevant stakeholders. Quantitative data were analysed using the Pingouin 0.53 python package. Qualitative data were examined with word frequency analysis and analysed for themes with grounded theory with a patient expert.

RESULTS

184 participants were recruited, (54, 60, 17, 12, 41 respectively). There were significant (p < 0.00001) differences in age, gender and ethnicity between groups. Key themes emerging from our results were the importance fast and accurate results, explanations over model performance and the significance of maintaining personal connections and choice. These themes were used to construct the ethical matrix.

CONCLUSION

The ethical matrix is a useful tool for PPI and stakeholder engagement with particular advantages for near-term AI in the pandemic era.

IMPLICATIONS FOR PRACTICE

We have produced an ethical matrix that allows for the inclusion of stakeholder opinion in medical AI research design.

A unified framework for focal intensity change detection and deformable image registration. Application to the monitoring of multiple sclerosis lesions in longitudinal 3D brain MRI

Registration is a crucial step in the design of automatic change detection methods dedicated to longitudinal brain MRI. Even small registration inaccuracies can significantly deteriorate the detection performance by introducing numerous spurious detections. Rigid or affine registration are usually considered to align baseline and follow-up scans, as a pre-processing step before applying a change detection method. In the context of multiple sclerosis, using deformable registration can be required to capture the complex deformations due to brain atrophy. However, non-rigid registration can alter the shape of appearing and evolving lesions while minimizing the dissimilarity between the two images. To overcome this issue, we consider registration and change detection as intertwined problems that should be solved jointly. To this end, we formulate these two separate tasks as a single optimization problem involving a unique energy that models their coupling. We focus on intensity-based change detection and registration, but the approach is versatile and could be extended to other modeling choices. We show experimentally on synthetic and real data that the proposed joint approach overcomes the limitations of the sequential scheme.

Improved Detection of New MS Lesions during Follow-Up Using an Automated MR Coregistration-Fusion Method

BACKGROUND AND PURPOSE

MR imaging is the key examination in the follow-up of patients with MS, by identification of new high-signal T2 brain lesions. However, identifying new lesions when scrolling through 2 follow-up MR images can be difficult and time-consuming. Our aim was to compare an automated coregistration-fusion reading approach with the standard approach by identifying new high-signal T2 brain lesions in patients with multiple sclerosis during follow-up MR imaging.

MATERIALS AND METHODS

This prospective monocenter study included 94 patients (mean age, 38.9 years) treated for MS with dimethyl fumarate from January 2014 to August 2016. One senior neuroradiologist and 1 junior radiologist checked for new high-signal T2 brain lesions, independently analyzing blinded image datasets with automated coregistration-fusion or the standard scroll-through approach with a 3-week delay between the 2 readings. A consensus reading with a second senior neuroradiologist served as a criterion standard for analyses. A Poisson regression and logistic and γ regressions were used to compare the 2 methods. Intra- and interobserver agreement was assessed by the κ coefficient.

RESULTS

There were significantly more new high-signal T2 lesions per patient detected with the coregistration-fusion method (7 versus 4, P < .001). The coregistration-fusion method detected significantly more patients with at least 1 new high-signal T2 lesion (59% versus 46%, P = .02) and was associated with significantly faster overall reading time (86 seconds faster, P < .001) and higher reader confidence (91% versus 40%, P < 1 × 10^-4). Inter- and intraobserver agreement was excellent for counting new high-signal T2 lesions.

CONCLUSIONS

Our study showed that an automated coregistration-fusion method was more sensitive for detecting new high-signal T2 lesions in patients with MS and reducing reading time. This method could help to improve follow-up care.