Dataset Information

Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy.

ABSTRACT: Despite the recognized importance of atrophy in multiple sclerosis (MS), methods for its quantification have been mostly restricted to the research domain. Recently, a CE labelled and FDA approved MS-specific atrophy quantification method, MSmetrix, has become commercially available. Here we perform a validation of MSmetrix against established methods in simulated and in vivo MRI data.Whole-brain and gray matter (GM) volume were measured with the cross-sectional pipeline of MSmetrix and compared to the outcomes of FreeSurfer (cross-sectional pipeline), SIENAX and SPM. For this comparison we investigated 20 simulated brain images, as well as in vivo data from 100 MS patients and 20 matched healthy controls. In fifty of the MS patients a second time point was available. In this subgroup, we additionally analyzed the whole-brain and GM volume change using the longitudinal pipeline of MSmetrix and compared the results with those of FreeSurfer (longitudinal pipeline) and SIENA.In the simulated data, SIENAX displayed the smallest average deviation compared with the reference whole-brain volume (+ 19.56 ± 10.34 mL), followed by MSmetrix (- 38.15 ± 17.77 mL), SPM (- 42.99 ± 17.12 mL) and FreeSurfer (- 78.51 ± 12.68 mL). A similar pattern was seen in vivo. Among the cross-sectional methods, Deming regression analyses revealed proportional errors particularly in MSmetrix and SPM. The mean difference percentage brain volume change (PBVC) was lowest between longitudinal MSmetrix and SIENA (+ 0.16 ± 0.91%). A strong proportional error was present between longitudinal percentage gray matter volume change (PGVC) measures of MSmetrix and FreeSurfer (slope = 2.48). All longitudinal methods were sensitive to the MRI hardware upgrade that occurred during the time of the study.MSmetrix, FreeSurfer, FSL and SPM show differences in atrophy measurements, even at the whole-brain level, that are large compared to typical atrophy rates observed in MS. Especially striking are the proportional errors between methods. Cross-sectional MSmetrix behaved similarly to SPM, both in terms of mean volume difference as well as proportional error. Longitudinal MSmetrix behaved most similar to SIENA. Our results indicate that brain volume measurement and normalization from T1-weighted images remains an unsolved problem that requires much more attention.

SUBMITTER: Steenwijk MD

PROVIDER: S-EPMC5540882 | biostudies-other | 2017

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy.

Steenwijk Martijn D MD Amiri Houshang H Schoonheim Menno M MM de Sitter Alexandra A Barkhof Frederik F Pouwels Petra J W PJW Vrenken Hugo H

NeuroImage. Clinical 20170630

<h4>Introduction</h4>Despite the recognized importance of atrophy in multiple sclerosis (MS), methods for its quantification have been mostly restricted to the research domain. Recently, a CE labelled and FDA approved MS-specific atrophy quantification method, MSmetrix, has become commercially available. Here we perform a validation of MSmetrix against established methods in simulated and <i>in vivo</i> MRI data.<h4>Methods</h4>Whole-brain and gray matter (GM) volume were measured with the cross ...[more]

PMID: 28794970

Similar Datasets

Project description:Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course and severity. Seventy-six MS patients [71% females, mean age 34.8 years (range 21-49) at inclusion] were examined with brain MRI at three time points with a mean total follow up period of 4.4 years (±0.4 years). We used additional cross-sectional MRI data from 235 HC for case-control comparison. We applied a machine learning model trained on an independent set of 3,208 HC to estimate individual brain age and to calculate the difference between estimated and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in individuals with MS. MS patients showed significantly higher BAG (4.4 ± 6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 × 10-6). Longitudinal estimates of BAG in MS patients showed high reliability and suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (SE = 0.15) years compared to chronological aging (p = 0.008). Multiple regression analyses revealed higher rate of brain aging in patients with more brain atrophy (Cohen's D = 0.86, p = 4.3 × 10-15) and increased white matter lesion load (WMLL) (Cohen's D = 0.55, p = 0.015). On average, patients with MS had significantly higher BAG compared to HC. Progressive brain aging in patients with MS was related to brain atrophy and increased WMLL. No significant clinical associations were found in our sample, future studies are warranted on this matter. Brain age estimation is a promising method for evaluation of subtle brain changes in MS, which is important for predicting clinical outcome and guide choice of intervention.

Project description:BackgroundEfforts to monitor malaria transmission increasingly use cross-sectional surveys to estimate transmission intensity from seroprevalence data using malarial antibodies. To date, seroconversion rates estimated from cross-sectional surveys have not been compared to rates estimated in prospective cohorts. Our objective was to compare seroconversion rates estimated in a prospective cohort with those from a cross-sectional survey in a low-transmission population.Methods and findingsThe analysis included two studies from Haiti: a prospective cohort of 142 children ages ≤ 11 years followed for up to 9 years, and a concurrent cross-sectional survey of 383 individuals ages 0-90 years old. From all individuals, we analyzed 1,154 blood spot specimens for the malaria antibody MSP-1(19) using a multiplex bead antigen assay. We classified individuals as positive for malaria using a cutoff derived from the mean plus 3 standard deviations in antibody responses from a negative control set of unexposed individuals. We estimated prospective seroconversion rates from the longitudinal cohort based on 13 incident seroconversions among 646 person-years at risk. We also estimated seroconversion rates from the cross-sectional survey using a reversible catalytic model fit with maximum likelihood. We found the two approaches provided consistent results: the seroconversion rate for ages ≤ 11 years was 0.020 (0.010, 0.032) estimated prospectively versus 0.023 (0.001, 0.052) in the cross-sectional survey.ConclusionsThe estimation of seroconversion rates using cross-sectional data is a widespread and generalizable problem for many infectious diseases that can be measured using antibody titers. The consistency between these two estimates lends credibility to model-based estimates of malaria seroconversion rates using cross-sectional surveys. This study also demonstrates the utility of including malaria antibody measures in multiplex assays alongside targets for vaccine coverage and other neglected tropical diseases, which together could comprise an integrated, large-scale serological surveillance platform.

Dataset Information

Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy.

Publications

Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets