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Aging Neurosci."],"abstract":"<jats:p><jats:bold>Introduction:<\/jats:bold> White matter hyperintensities of presumed vascular origin (WMH) are an important magnetic resonance imaging marker of cerebral small vessel disease and are associated with cognitive decline, stroke, and mortality. Their relevance in healthy individuals, however, is less clear. This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; <jats:ext-link>https:\/\/fsl.fmrib.ox.ac.uk\/fsl\/fslwiki<\/jats:ext-link>) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> We evaluated the effect of varying training sample sizes on the accuracy and the robustness of the predicted white matter hyperintensity volume in a population (<jats:italic>n<\/jats:italic> = 201) with a low prevalence of confluent WMH and a substantial proportion of participants without WMH. BIANCA was trained with seven different sample sizes between 10 and 40 with increments of 5. For each sample size, 100 random samples of T1w and FLAIR images were drawn and trained with manually delineated masks. For validation, we defined an internal and external validation set and compared the mean absolute error, resulting from the difference between manually delineated and predicted WMH volumes for each set. For spatial overlap, we calculated the Dice similarity index (SI) for the external validation cohort.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The study population had a median WMH volume of 0.34 ml (IQR of 1.6 ml) and included <jats:italic>n<\/jats:italic> = 28 (18%) participants without any WMH. The mean absolute error of the difference between BIANCA prediction and manually delineated masks was minimized and became more robust with an increasing number of training participants. The lowest mean absolute error of 0.05 ml (SD of 0.24 ml) was identified in the external validation set with a training sample size of 35. Compared to the volumetric overlap, the spatial overlap was poor with an average Dice similarity index of 0.14 (SD 0.16) in the external cohort, driven by subjects with very low lesion volumes.<\/jats:p><jats:p><jats:bold>Discussion:<\/jats:bold> We found that the performance of BIANCA, particularly the robustness of predictions, could be optimized for use in populations with a low WMH load by enlargement of the training sample size. Further work is needed to evaluate and potentially improve the prediction accuracy for low lesion volumes. These findings are important for current and future population-based studies with the majority of participants being normal aging people.<\/jats:p>","DOI":"10.3389\/fnagi.2021.720636","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T04:31:00Z","timestamp":1641875460000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA"],"prefix":"10.3389","volume":"13","author":[{"given":"Niklas","family":"Wulms","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lea","family":"Redmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christine","family":"Herpertz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadine","family":"Bonberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klaus","family":"Berger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benedikt","family":"Sundermann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heike","family":"Minnerup","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/j.neuroimage.2005.06.061","article-title":"Fully automatic segmentation of white matter hyperintensities in MR images of the elderly","volume":"28","author":"Admiraal-Behloul","year":"2005","journal-title":"Neuroimage"},{"key":"B2","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/s00103-020-03111-0","article-title":"The investigation of health outcomes in the German National Cohort: the most relevant endpoints and their assessment","volume":"63","author":"Ahrens","year":"2020","journal-title":"Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz"},{"key":"B3","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.neuroimage.2017.10.034","article-title":"Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank","volume":"166","author":"Alfaro-Almagro","year":"2018","journal-title":"Neuroimage"},{"key":"B4","author":"Allaire","year":"2019","journal-title":"Rmarkdown: Dynamic Documents for R"},{"key":"B5","doi-asserted-by":"publisher","first-page":"63","DOI":"10.12688\/wellcomeopenres.15191.1","article-title":"Raincloud plots: a multi-platform tool for robust data visualization [version 1; peer review: 2 approved]","volume":"4","author":"Allen","year":"2019","journal-title":"Wellcome Open Res."},{"key":"B6","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1016\/j.neuroimage.2003.10.012","article-title":"Probabilistic segmentation of white matter lesions in MR imaging","volume":"21","author":"Anbeek","year":"2004","journal-title":"Neuroimage"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1148\/radiol.2015142272","article-title":"Whole-body MR imaging in the German national cohort: rationale, design, and technical background","volume":"277","author":"Bamberg","year":"2015","journal-title":"Radiology"},{"key":"B8","doi-asserted-by":"publisher","first-page":"8242","DOI":"10.1038\/s41598-020-64803-w","article-title":"Evaluating white matter lesion segmentations with refined S\u00f8rensen-dice analysis","volume":"10","author":"Carass","year":"2020","journal-title":"Sci. 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