AUTHOR=Shao Yuan , Chen Zhonghua , Ming Shuai , Ye Qin , Shu Zhenyu , Gong Cheng , Pang Peipei , Gong Xiangyang 

TITLE=Predicting the Development of Normal-Appearing White Matter With Radiomics in the Aging Brain: A Longitudinal Clinical Study

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=10

YEAR=2018

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2018.00393

DOI=10.3389/fnagi.2018.00393

ISSN=1663-4365

ABSTRACT=<p><bold>Background:</bold> Normal-appearing white matter (NAWM) refers to the normal, yet diseased tissue around the white matter hyperintensities (WMH) on conventional MR images. Radiomics is an emerging quantitative imaging technique that provides more details than a traditional visual analysis. This study aims to explore whether WMH could be predicted during the early stages of NAWM, using a textural analysis in the general elderly population.</p><p><bold>Methods:</bold> Imaging data were obtained from PACS between 2012 and 2017. The subjects (≥60 years) received two or more MRI exams on the same scanner with time intervals of more than 1 year. By comparing the baseline and follow-up images, patients with noted progression of WMH were included as the case group (<italic>n</italic> = 51), while age-matched subjects without WMH were included as the control group (<italic>n</italic> = 51). Segmentations of the regions of interest (ROIs) were done with the ITK software. Two ROIs of developing NAWM (dNAWM) and non-developing NAWM (non-dNAWM) were drawn separately on the FLAIR images of each patient. dNAWM appeared normal on the baseline images, yet evolved into WMH on the follow-up images. Non-dNAWM appeared normal on both the baseline and follow-up images. A third ROI of normal white matter (NWM) was extracted from the control group, which was normal on both baseline and follow-up images. Textural features were dimensionally reduced with ANOVA+MW, correlation analysis, and LASSO. Three models were built based on the optimal parameters of dimensional reduction, including Model 1 (NWM vs. dNAWM), Model 2 (non-dNAWM vs. dNAWM), and Model 3 (NWM vs. non-dNAWM). The ROC curve was adopted to evaluate the classification validity of these models.</p><p><bold>Results:</bold> Basic characteristics of the patients and controls showed no significant differences. The AUC of Model 1 in training and test groups were 0.967 (95% CI: 0.831–0.999) and 0.954 (95% CI: 0.876–0.989), respectively. The AUC of Model 2 were 0.939 (95% CI: 0.856–0.982) and 0.846 (95% CI: 0.671–0.950). The AUC of Model 3 were 0.713 (95% CI: 0.593–0.814) and 0.667 (95% CI: 0.475–0.825).</p><p><bold>Conclusion:</bold> Radiomics textural analysis can distinguish dNAWM from non-dNAWM on FLAIR images, which could be used for the early detection of NAWM lesions before they develop into visible WHM.</p>