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Within-patient fluctuation of brain volume estimates from short-term repeated MRI measurements using SIENA/FSL.
Journal of Neurology 2018 May
BACKGROUND: Measurements of brain volume loss (BVL) in individual patients are currently discussed controversially. One concern is the impact of short-term biological noise, like hydration status.
METHODS: Three publicly available reliability MRI datasets with scan intervals of days to weeks were used. An additional cohort of 60 early relapsing multiple sclerosis (MS) patients with MRI follow-ups was analyzed to test whether after 1 year pathological BVL is detectable in a relevant fraction of MS patients. BVL was determined using SIENA/FSL. Results deviating from zero in the reliability datasets were considered as within-patient fluctuation (WPF) consisting of the intrinsic measurement error as well as the short-term biological fluctuations of brain volumes. We provide an approach to interpret BVL measurements in individual patients taking the WPF into account.
RESULTS: The estimated standard deviation of BVL measurements from the pooled reliability datasets was 0.28%. For a BVL measurement of x% per year in an individual patient, the true BVL lies with an error probability of 5% in the interval x% ± (1.96 × 0.28)/(scan interval in years)%. To allow a BVL per year of at least 0.4% to be identified after 1 year, the measured BVL needs to exceed 0.94%. The median BVL per year in the MS patient cohort was 0.44%. In 11 out of 60 MS patients (18%) we found a BVL per year equal or greater than 0.94%.
CONCLUSION: The estimated WPF may be helpful when interpreting BVL results on an individual patient level in diseases such as MS.
METHODS: Three publicly available reliability MRI datasets with scan intervals of days to weeks were used. An additional cohort of 60 early relapsing multiple sclerosis (MS) patients with MRI follow-ups was analyzed to test whether after 1 year pathological BVL is detectable in a relevant fraction of MS patients. BVL was determined using SIENA/FSL. Results deviating from zero in the reliability datasets were considered as within-patient fluctuation (WPF) consisting of the intrinsic measurement error as well as the short-term biological fluctuations of brain volumes. We provide an approach to interpret BVL measurements in individual patients taking the WPF into account.
RESULTS: The estimated standard deviation of BVL measurements from the pooled reliability datasets was 0.28%. For a BVL measurement of x% per year in an individual patient, the true BVL lies with an error probability of 5% in the interval x% ± (1.96 × 0.28)/(scan interval in years)%. To allow a BVL per year of at least 0.4% to be identified after 1 year, the measured BVL needs to exceed 0.94%. The median BVL per year in the MS patient cohort was 0.44%. In 11 out of 60 MS patients (18%) we found a BVL per year equal or greater than 0.94%.
CONCLUSION: The estimated WPF may be helpful when interpreting BVL results on an individual patient level in diseases such as MS.
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