Clinico-radiological dissociation of disease activity in MS patients: frequency and clinical relevance

Neutrophil/lymphocyte and monocyte/lymphocyte indexes as potential predictors of relapse at 1 year after diagnosis of pediatric multiple sclerosis: a single-center, exploratory and proof-of-concept study

Introduction

Early identification of patients with a more unfavorable outcome in Multiple Sclerosis (MS) is crucial to optimize individualized treatment. Neutrophil-lymphocyte index (NLI) and monocyte-lymphocyte index (MLI) have been considered as potential biomarkers for disease prognosis. Our study aims to investigate the usefulness of NLI and MLI as predictors of relapse, disability progression, and lesion accumulation on magnetic resonance imaging (MRI) 1 year after diagnosis and treatment initiation, in pediatric-onset MS.

Methods

A retrospective single-center study was conducted, including patients with diagnosis of MS established in pediatric age (<18 years old), at least 1-year of follow-up, and a complete blood count (CBC) performed at diagnosis. We collected the nearest-to-diagnosis NLI and MLI, as well as clinical and imaging variables, at diagnosis and 12 months later. Our cohort was further dichotomized into two groups, based on the presence of relapses. Statistical significance was considered for p < 0.05.

Results

Eighteen patients (n = 18) were included. The relapsing group had higher mean, minimum, and maximum values for both NLI (5.17 ± 5.85, range: 1.57-11.92) and MLI (0.35 ± 0.22, range: 0.19-0.59), compared to the non-relapsing group (2.19 ± 1.63, range: 1.12-7.32 for NLI, and 0.24 ± 0.09, range: 0.14-0.44 for MLI). A higher percentage of patients in the relapsing group had increased NLI (>1.89, 66.7%) and MLI (>0.21, 66.7%) values than those in the non-relapsing group (46.7%). Patients who presented new T2-hyperintense lesions on MRI after 1 year of follow-up also had higher mean, minimum, and maximum values of both biomarkers. Patients who did not achieve No Evidence of Disease Activity-3 (NEDA-3) state exhibited higher values for both ratios. However, in our sample, no statistically significant correlations were found between MLI and NLI values and the clinical and imaging variables considered.

Conclusion

The ease of obtaining NLI and MLI from routine blood tests renders them useful biomarkers as a screening tool in longitudinal follow-up. Our study was based on a very small sample size, but it allowed us to verify the feasibility of the protocol used. It is intended to involve other centers in the next phase of this work, testing the possible usefulness of the indices under analysis on a larger sample.

Minimum detectable spinal cord atrophy with automatic segmentation: Investigations using an open-access dataset of healthy participants

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Evaluate the robustness of an automated analysis pipeline for detecting SC atrophy.

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Simulate spinal cord atrophy and scan-rescan variability.

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Fully automated analysis method available on an open access database.

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Evaluation of sample size and inter/intra-subject variability for T1w and T2w images.

Spinal cord atrophy is a well-known biomarker in multiple sclerosis (MS) and other diseases. It is measured by segmenting the spinal cord on an MRI image and computing the average cross-sectional area (CSA) over a few slices. Introduced about 25 years ago, this procedure is highly sensitive to the quality of the segmentation and is prone to rater-bias. Recently, fully-automated spinal cord segmentation methods, which remove the rater-bias and enable the automated analysis of large populations, have been introduced. A lingering question related to these automated methods is: How reliable are they at detecting atrophy? In this study, we evaluated the precision and accuracy of automated atrophy measurements by simulating scan-rescan experiments.

Spinal cord MRI data from the open-access spine-generic project were used. The dataset aggregates 42 sites worldwide and consists of 260 healthy subjects and includes T1w and T2w contrasts. To simulate atrophy, each volume was globally rescaled at various scaling factors. Moreover, to simulate patient repositioning, random rigid transformations were applied. Using the DeepSeg algorithm from the Spinal Cord Toolbox, the spinal cord was segmented and vertebral levels were identified. Then, the average CSA between C3-C5 vertebral levels was computed for each Monte Carlo sample, allowing us to derive measures of atrophy, intra/inter-subject variability, and sample-size calculations.

The minimum sample size required to detect an atrophy of 2% between unpaired study arms, commonly seen in MS studies, was 467 +/− 13.9 using T1w and 467 +/− 3.2 using T2w images. The minimum sample size to detect a longitudinal atrophy (between paired study arms) of 0.8% was 60 +/− 25.1 using T1w and 10 +/− 1.2 using T2w images. At the intra-subject level, the estimated CSA, observed in this study, showed good precision compared to other studies with COVs (across Monte Carlo transformations) of 0.8% for T1w and 0.6% for T2w images.

While these sample sizes seem small, we would like to stress that these results correspond to a “best case” scenario, in that the dataset used here was of particularly good quality and the model for simulating atrophy does not encompass all the variability met in real-life datasets. The simulated atrophy and scan-rescan variability may over-simplify the biological reality. The proposed framework is open-source and available at https://csa-atrophy.readthedocs.io/.