Predicting the Development of Normal-Appearing White Matter With Radiomics in the Aging Brain: A Longitudinal Clinical Study
Predicting the Development of Normal-Appearing White Matter With Radiomics in the Aging Brain: A Longitudinal Clinical Study
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DOI：
10.3389/fnagi.2018.00393
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发表时间：
2018-11-28
影响因子：
4.8
通讯作者：
Gong, Xiangyang
中科院分区：
文献类型：
--
作者：
Shao, Yuan;Chen, Zhonghua;Gong, Xiangyang
背景：外观正常的白质（NAWM）是指常规MR图像上白质高信号（WMH）周围正常但病变的组织。放射组学是一种新兴的定量成像技术，它比传统的视觉分析提供更多的细节。本研究旨在探讨WMH是否可以在NAWM的早期阶段进行预测，通过对一般老年人群进行结构分析。方法：2012 - 2017年PACS影像资料。受试者（>= 60岁）在同一台扫描仪上接受两次或两次以上MRI检查，时间间隔超过1年。通过比较基线和随访图像，有明显进展的WMH患者被纳入病例组（n = 51），而年龄匹配的无WMH患者被纳入对照组（n = 51）。利用ITK软件对感兴趣区域（roi）进行分割。在每个患者的FLAIR图像上分别绘制发生性NAWM （dNAWM）和非发生性NAWM （non-dNAWM）的roi。dNAWM在基线图像上表现正常，但在随访图像上演变为WMH。非dnawm在基线和随访图像上显示正常。从对照组中提取第三个正常白质ROI (NWM)，其在基线和随访图像上均正常。采用方差分析+MW、相关分析和LASSO对纹理特征进行降维。基于降维的最优参数，构建了模型1 （NWM vs. dNAWM）、模型2（非dNAWM vs. dNAWM）和模型3 （NWM vs.非dNAWM）。采用ROC曲线评价模型的分类效度。结果：患者与对照组基本特征无显著差异。模型1在训练组和试验组的AUC分别为0.967 （95% CI: 0.831-0.999）和0.954 （95% CI: 0.876-0.989）。模型2的AUC分别为0.939 （95% CI: 0.856 ~ 0.982）和0.846 （95% CI: 0.671 ~ 0.950）。模型3的AUC分别为0.713 （95% CI: 0.593-0.814）和0.667 （95% CI: 0.475-0.825）。结论：放射组学结构分析可以在FLAIR图像上区分dNAWM与非dNAWM，可用于在NAWM病变发展为可见WHM之前的早期发现。
Background: 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.Methods: 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 (n = 51), while age-matched subjects without WMH were included as the control group (n = 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.Results: 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).Conclusion: 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.