Comparison of qualitative and fully automated quantitative tools for classifying severity of white matter hyperintensity.

OBJECTIVE: In this study, we aimed to compare the Fazekas scoring system and quantitative white matter hyperintensity volume in the classification of white matter hyperintensity severity using a fully automated analysis software to investigate the reliability of quantitative evaluation. MATERIALS AND METHODS: Patients with suspected cognitive impairment who underwent medical examinations at our institution between January 2010 and May 2021 were retrospectively examined. White matter hyperintensity volumes were analyzed using fully automated analysis software and Fazekas scoring (scores 0-3). Using one-way analysis of variance, white matter hyperintensity volume differences across Fazekas scores were assessed. We employed post-hoc pairwise comparisons to compare the differences in the mean white matter hyperintensity volume between each Fazekas score. Spearman's rank correlation test was used to investigate the association between Fazekas score and white matter hyperintensity volume. RESULTS: Among the 839 patients included in this study, Fazekas scores 0, 1, 2, and 3 were assigned to 68, 198, 217, and 356 patients, respectively. White matter hyperintensity volumes significantly differed according to Fazekas score (F=623.5, p<0.001). Post-hoc pairwise comparisons revealed significant differences in mean white matter hyperintensity volume between all Fazekas scores (p<0.05). We observed a significantly positive correlation between the Fazekas scores and white matter hyperintensity volume (R=0.823, p<0.01). CONCLUSIONS: Quantitative white matter hyperintensity volume and the Fazekas scores are highly correlated and may be used as indicators of white matter hyperintensity severity. In addition, quantitative analysis may be more effective in classifying advanced white matter hyperintensity lesions than the Fazekas classification.

[Echo Train Length (ETL) of Fluid-attenuated Inversion Recovery (FLAIR) and Extraction Volume of White Matter Hyperintensity Volume in Automated White Matter Signal Analysis].

PURPOSE: To investigate whether the volume of white matter hyperintensity (WMH) extracted from FLAIR images changes when the imaging parameters of the original images are changed. METHODS: Seven healthy volunteers were imaged by changing the imaging parameter ETL of FLAIR images, and WMHs were extracted and their volumes were calculated by the automatic extraction software. The results were statistically analyzed to examine the relationship (Experiment 1). Simulated images with different SNRs were created by adding white noise to four examples of healthy volunteer images. The SNR of the simulated images simulated the SNR of the measured images of different ETLs. The WMH was extracted from the simulated images and its volume was calculated using the automatic extraction software (Experiment 2). RESULTS: Experiment 1 showed that there was no significant difference between FLAIR imaging parameters and WMH volume in automatic white matter signal analysis, except for some conditions. Experiment 2 showed that as the SNR of the original image decreased, the volume of high white matter signal extracted decreased. CONCLUSION: In automatic white matter signal analysis, WMH was shown to be small when the ETL of the FLAIR sequence was larger than normal and/or the SNR of the image was low.