The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA - Wulms, Niklas; Redmann, Lea; Herpertz, Christine; Bonberg, Nadine; Berger, Klaus; Sundermann, Benedikt; Minnerup, Heike (Frontiers in Aging Neuroscience, 2022)
Introduction: White matter hyperintensities (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. We here tested whether WMH volumetry with FSL BIANCA, a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population. Methods: We evaluated the effect of varying training sample size on the accuracy of the predicted white matter hyperintensity volume in a population (n = 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 we compared the mean absolute error, resulting from the difference between manually delineated and predicted WMH volumes for each set. Results: The study population had a median WMH volume of 0.34 ml (IQR of 1.6 ml) and included n = 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. Discussion: We found that the performance of BIANCA could be optimized for the use in populations with a low WMH load by enlargement of the training sample size. These findings are important for current and future population-based studies with the majority of participants being normal aging men and women.