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Ronald A. van Schijndel

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5 papers

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YNICL Journal 2023 Journal Article

The spatio-temporal relationship between concurrent lesion and brain atrophy changes in early multiple sclerosis: A post-hoc analysis of the REFLEXION study

Giordano Gentile
Rozemarijn M. Mattiesing
Iman Brouwer
Ronald A. van Schijndel
Bernard M.J. Uitdehaag
Jos W.R. Twisk
Ludwig Kappos
Mark S. Freedman

BACKGROUND: White matter (WM) lesions and brain atrophy are present early in multiple sclerosis (MS). However, their spatio-temporal relationship remains unclear. METHODS: Yearly magnetic resonance images were analysed in 387 patients with a first clinical demyelinating event (FCDE) from the 5-year REFLEXION study. Patients received early (from baseline; N = 258; ET) or delayed treatment (from month-24; N = 129; DT) with subcutaneous interferon beta-1a. FSL-SIENA/VIENA were used to provide yearly percentage volume change of brain (PBVC) and ventricles (PVVC). Yearly total lesion volume change (TLVC) was determined by a semi-automated method. Using linear mixed models and voxel-wise analyses, we firstly investigated the overall relationship between TLVC and PBVC and between TLVC and PVVC in the same follow-up period. Analyses were then separately performed for: the untreated period of DT patients (first two years), the first year of treatment (year 1 for ET and year 3 for DT), and a period where patients had received at least 1 year of treatment (stable treatment; ET: years 2, 3, 4, and 5; DT: years 4 and 5). RESULTS: Whole brain: across the whole study period, lower TLVC was related to faster atrophy (PBVC: B = 0.046, SE = 0.013, p < 0.001; PVVC: B = -0.466, SE = 0.118, p < 0.001). Within the untreated period of DT patients, lower TLVC was related to faster atrophy (PBVC: B = 0.072, SE = 0.029, p = 0.013; PVVC: B = -0.917, SE = 0.306, p = 0.003). A similar relationship was found within the first year of treatment of ET patients (PBVC: B = 0.081, SE = 0.027, p = 0.003; PVVC: B = -1.08, SE = 0.284, p < 0.001), consistent with resolving oedema and pseudo-atrophy. Voxel-wise: overall, higher TLVC was related to faster ventricular enlargement. Lower TLVC was related to faster widespread atrophy in year 1 in both ET (first year of treatment) and DT (untreated) patients. In the second untreated year of DT patients and within the stable treatment period of ET patients (year 4), faster periventricular and occipital lobe atrophy was associated with higher TLVC. CONCLUSIONS: WM lesion changes and atrophy occurred simultaneously in early MS. Spatio-temporal correspondence of these two processes involved mostly the periventricular area. Within the first year of the study, in both treatment groups, faster atrophy was linked to lower lesion volume changes, consistent with higher shrinking and disappearing lesion activity. This might reflect the pseudo-atrophy phenomenon that is probably related to the therapy driven (only in ET patients, as they received treatment from baseline) and "natural" (both ET and DT patients entered the study after a FCDE) resolution of oedema. In an untreated period and later on during stable treatment, (real) atrophy was related to higher lesion volume changes, consistent with increased new and enlarging lesion activity.

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YNICL Journal 2022 Journal Article

The spatio-temporal relationship between white matter lesion volume changes and brain atrophy in clinically isolated syndrome and early multiple sclerosis

Rozemarijn M. Mattiesing
Giordano Gentile
Iman Brouwer
Ronald A. van Schijndel
Bernard M.J. Uitdehaag
Jos W.R. Twisk
Ludwig Kappos
Mark S. Freedman

BACKGROUND: White matter lesions and brain atrophy are both present early in multiple sclerosis. However, the spatio-temporal relationship between atrophy and lesion processes remains unclear. METHODS: Yearly magnetic resonance images were analyzed in 392 patients with clinically isolated syndrome from the 5-year REFLEX/REFLEXION studies. Patients received early treatment (from baseline; N = 262) or delayed treatment (from month-24; N = 130) with subcutaneous interferon beta-1a. Global and central atrophy were assessed using FSL-SIENA to provide yearly percentage volume change of brain and ventricles, respectively. Yearly total lesion volume change was calculated by subtracting the sum of the negative lesion volume change (disappearing + shrinking) from the positive lesion volume change (new + enlarging) for each yearly interval, as determined by an in-house developed semi-automated method. Using linear mixed models, during the period where patients had received ≥1 year of treatment, we investigated whether total lesion volume change was associated with percentage brain volume change or percentage ventricular volume change in the next year, and vice versa. RESULTS: Higher total lesion volume change was related to significantly faster global atrophy (percentage brain volume change) in the next year (B = - 0.113, SE = 0.022, p < 0.001). In patients receiving early treatment only, total lesion volume change was also associated with percentage ventricular volume change in the next year (B = 1.348, SE = 0.181, p < 0.001). Voxel-wise analyses showed that in patients receiving early treatment, higher total lesion volume change in years 2, 3, and 4 was related to faster atrophy in the next year, and in year 4 this relationship was stronger in patients receiving delayed treatment. Interestingly, faster atrophy was related to higher total lesion volume change in the next year (percentage brain volume change: B = - 0.136, SE = 0.062, p = 0.028; percentage ventricular volume change: B = 0.028, SE = 0.008, p < 0.001). CONCLUSIONS: Higher lesion volume changes were associated with faster atrophy in the next year. Interestingly, there was also an association between faster atrophy and higher lesion volume changes in the next year.

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YNIMG Journal 2017 Journal Article

Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study

Alexandra de Sitter
Martijn D. Steenwijk
Aurélie Ruet
Adriaan Versteeg
Yaou Liu
Ronald A. van Schijndel
Petra J.W. Pouwels
Iris D. Kilsdonk

Background and Purpose In vivoidentification of white matter lesions plays a key-role in evaluation of patients with multiple sclerosis (MS). Automated lesion segmentation methods have been developed to substitute manual outlining, but evidence of their performance in multi-center investigations is lacking. In this work, five research-domain automated segmentation methods were evaluated using a multi-center MS dataset. Methods 70 MS patients (median EDSS of 2. 0 [range 0. 0–6. 5]) were included from a six-center dataset of the MAGNIMS Study Group (www. magnims. eu) which included 2D FLAIR and 3D T1 images with manual lesion segmentation as a reference. Automated lesion segmentations were produced using five algorithms: Cascade; Lesion Segmentation Toolbox (LST) with both the Lesion growth algorithm (LGA) and the Lesion prediction algorithm (LPA); Lesion-Topology preserving Anatomical Segmentation (Lesion-TOADS); and k-Nearest Neighbor with Tissue Type Priors (kNN-TTP). Main software parameters were optimized using a training set (N = 18), and formal testing was performed on the remaining patients (N = 52). To evaluate volumetric agreement with the reference segmentations, intraclass correlation coefficient (ICC) as well as mean difference in lesion volumes between the automated and reference segmentations were calculated. The Similarity Index (SI), False Positive (FP) volumes and False Negative (FN) volumes were used to examine spatial agreement. All analyses were repeated using a leave-one-center-out design to exclude the center of interest from the training phase to evaluate the performance of the method on ‘unseen’ center. Results Compared to the reference mean lesion volume (4. 85 ± 7. 29 mL), the methods displayed a mean difference of 1. 60 ± 4. 83 (Cascade), 2. 31 ± 7. 66 (LGA), 0. 44 ± 4. 68 (LPA), 1. 76 ± 4. 17 (Lesion-TOADS) and −1. 39 ± 4. 10 mL (kNN-TTP). The ICCs were 0. 755, 0. 713, 0. 851, 0. 806 and 0. 723, respectively. Spatial agreement with reference segmentations was higher for LPA (SI = 0. 37 ± 0. 23), Lesion-TOADS (SI = 0. 35 ± 0. 18) and kNN-TTP (SI = 0. 44 ± 0. 14) than for Cascade (SI = 0. 26 ± 0. 17) or LGA (SI = 0. 31 ± 0. 23). All methods showed highly similar results when used on data from a center not used in software parameter optimization. Conclusion The performance of the methods in this multi-center MS dataset was moderate, but appeared to be robust even with new datasets from centers not included in training the automated methods.

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YNIMG Journal 2014 Journal Article

Hippocampal volume change measurement: Quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST

Emma R. Mulder
Remko A. de Jong
Dirk L. Knol
Ronald A. van Schijndel
Keith S. Cover
Pieter J. Visser
Frederik Barkhof
Hugo Vrenken

Background To measure hippocampal volume change in Alzheimer's disease (AD) or mild cognitive impairment (MCI), expert manual delineation is often used because of its supposed accuracy. It has been suggested that expert outlining yields poorer reproducibility as compared to automated methods, but this has not been investigated. Aim To determine the reproducibilities of expert manual outlining and two common automated methods for measuring hippocampal atrophy rates in healthy aging, MCI and AD. Methods From the Alzheimer's Disease Neuroimaging Initiative (ADNI), 80 subjects were selected: 20 patients with AD, 40 patients with mild cognitive impairment (MCI) and 20 healthy controls (HCs). Left and right hippocampal volume change between baseline and month-12 visit was assessed by using expert manual delineation, and by the automated software packages FreeSurfer (longitudinal processing stream) and FIRST. To assess reproducibility of the measured hippocampal volume change, both back-to-back (BTB) MPRAGE scans available for each visit were analyzed. Hippocampal volume change was expressed in μL, and as a percentage of baseline volume. Reproducibility of the 1-year hippocampal volume change was estimated from the BTB measurements by using linear mixed model to calculate the limits of agreement (LoA) of each method, reflecting its measurement uncertainty. Using the delta method, approximate p-values were calculated for the pairwise comparisons between methods. Statistical analyses were performed both with inclusion and exclusion of visibly incorrect segmentations. Results Visibly incorrect automated segmentation in either one or both scans of a longitudinal scan pair occurred in 7. 5% of the hippocampi for FreeSurfer and in 6. 9% of the hippocampi for FIRST. After excluding these failed cases, reproducibility analysis for 1-year percentage volume change yielded LoA of ±7. 2% for FreeSurfer, ±9. 7% for expert manual delineation, and ±10. 0% for FIRST. Methods ranked the same for reproducibility of 1-yearμL volume change, with LoA of ±218μL for FreeSurfer, ±319μL for expert manual delineation, and ±333μL for FIRST. Approximate p-values indicated that reproducibility was better for FreeSurfer than for manual or FIRST, and that manual and FIRST did not differ. Inclusion of failed automated segmentations led to worsening of reproducibility of both automated methods for 1-year raw and percentage volume change. Conclusion Quantitative reproducibility values of 1-year microliter and percentage hippocampal volume change were roughly similar between expert manual outlining, FIRST and FreeSurfer, but FreeSurfer reproducibility was statistically significantly superior to both manual outlining and FIRST after exclusion of failed segmentations.

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YNIMG Journal 2006 Journal Article

Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI

Ying Wu
Simon K. Warfield
I. Leng Tan
William M. Wells
Dominik S. Meier
Ronald A. van Schijndel
Frederik Barkhof
Charles R.G. Guttmann

Purpose. To automatically segment multiple sclerosis (MS) lesions into three subtypes (i. e. , enhancing lesions, T1 “black holes”, T2 hyperintense lesions). Materials and methods. Proton density-, T2- and contrast-enhanced T1-weighted brain images of 12 MR scans were pre-processed through intracranial cavity (IC) extraction, inhomogeneity correction and intensity normalization. Intensity-based statistical k-nearest neighbor (k-NN) classification was combined with template-driven segmentation and partial volume artifact correction (TDS+) for segmentation of MS lesions subtypes and brain tissue compartments. Operator-supervised tissue sampling and parameter calibration were performed on 2 randomly selected scans and were applied automatically to the remaining 10 scans. Results from this three-channel TDS+ (3ch-TDS+) were compared to those from a previously validated two-channel TDS+ (2ch-TDS+) method. The results of both the 3ch-TDS+ and 2ch-TDS+ were also compared to manual segmentation performed by experts. Results. Intra-class correlation coefficients (ICC) of 3ch-TDS+ for all three subtypes of lesions were higher (ICC between 0. 95 and 0. 96) than that of 2ch-TDS+ for T2 lesions (ICC = 0. 82). The 3ch-TDS+ also identified the three lesion subtypes with high specificity (98. 7–99. 9%) and accuracy (98. 5–99. 9%). Sensitivity of 3ch-TDS+ for T2 lesions was 16% higher than with 2ch-TDS+. Enhancing lesions were segmented with the best sensitivity (81. 9%). “Black holes” were segmented with the least sensitivity (62. 3%). Conclusion. 3ch-TDS+ is a promising method for automated segmentation of MS lesion subtypes.

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