Detection of Chronic Blast-Related Mild Traumatic Brain Injury with Diffusion Tensor Imaging and Support Vector Machines.
Detection of Chronic Blast-Related Mild Traumatic Brain Injury with Diffusion Tensor Imaging and Support Vector Machines.
复制标题
DOI：
10.3390/diagnostics12040987
复制
发表时间：
2022-04-14
期刊：
影响因子：
3.6
通讯作者：
Huang, Mingxiong
中科院分区：
文献类型：
--
作者：
Harrington, Deborah L.;Hsu, Po-Ya;Theilmann, Rebecca J.;Angeles-Quinto, Annemarie;Robb-Swan, Ashley;Nichols, Sharon;Song, Tao;Le, Lu;Rimmele, Carl;Matthews, Scott;Yurgil, Kate A.;Drake, Angela;Ji, Zhengwei;Guo, Jian;Cheng, Chung-Kuan;Lee, Roland R.;Baker, Dewleen G.;Huang, Mingxiong
关键词：
爆炸相关的轻度创伤性脑损伤（bmTBI）通常会导致长期后遗症，但由于对主要病理生理学的了解不足，缺乏诊断方法。本研究旨在通过将基于机器学习的支持向量机 (SVM) 建模应用于扩散张量成像 (DTI) 数据集来构建用于未来验证的诊断模型，以阐明区分 bmTBI 与健康对照 (HC) 的白质特征。 20 名亚急性/慢性 bmTBI 和 19 名 HC 战斗部署人员接受了 DTI。使用基于束的分析选择了建模的临床相关特征，该分析在五个 DTI 指标中识别了整个白质束的组差异，以阐明损伤的发病机制。然后使用支持向量机建模和交叉验证来分析这些特征。基于束的分析显示，bmTBI 组的径向扩散率 (RD) 异常降低，分数各向异性 (FA) 和轴向/径向扩散率比 (AD/RD) 增加，主要发生在前束（29 个特征）。 SVM 模型表明，前/上放射冠的 FA 以及胼胝体和内囊前肢的 AD/RD（5 个特征）能够最好地区分 bmTBI 与 HC，准确度为 89%。这是 SVM 的首次应用，仅根据明确定义的区域中的 DTI 指标来识别 bmTBI 的显着特征，如果成功验证，可以促进有针对性的治疗干预。
Blast-related mild traumatic brain injury (bmTBI) often leads to long-term sequalae, but diagnostic approaches are lacking due to insufficient knowledge about the predominant pathophysiology. This study aimed to build a diagnostic model for future verification by applying machine-learning based support vector machine (SVM) modeling to diffusion tensor imaging (DTI) datasets to elucidate white-matter features that distinguish bmTBI from healthy controls (HC). Twenty subacute/chronic bmTBI and 19 HC combat-deployed personnel underwent DTI. Clinically relevant features for modeling were selected using tract-based analyses that identified group differences throughout white-matter tracts in five DTI metrics to elucidate the pathogenesis of injury. These features were then analyzed using SVM modeling with cross validation. Tract-based analyses revealed abnormally decreased radial diffusivity (RD), increased fractional anisotropy (FA) and axial/radial diffusivity ratio (AD/RD) in the bmTBI group, mostly in anterior tracts (29 features). SVM models showed that FA of the anterior/superior corona radiata and AD/RD of the corpus callosum and anterior limbs of the internal capsule (5 features) best distinguished bmTBI from HCs with 89% accuracy. This is the first application of SVM to identify prominent features of bmTBI solely based on DTI metrics in well-defined tracts, which if successfully validated could promote targeted treatment interventions.
登录
查看更多内容
DOI：
10.1097/rmr.0000000000000062
发表时间：
2015-10
期刊：
Topics in magnetic resonance imaging : TMRI
影响因子：
--
作者：
Douglas DB;Iv M;Douglas PK;Anderson A;Vos SB;Bammer R;Zeineh M;Wintermark M
通讯作者：
Wintermark M
影响因子：
2.7
作者：
DePalma, Ralph G. ae;Hoffman, Stuart W.
通讯作者：
Hoffman, Stuart W.
影响因子：
3
作者：
Bigler, Erin D.
通讯作者：
Bigler, Erin D.
影响因子：
2.5
作者：
Costanzo, Michelle E.;Chou, Yi-Yu;Roy, Michael J.
通讯作者：
Roy, Michael J.
影响因子：
11.2
作者：
GENNARELLI, TA;THIBAULT, LE;MARCINCIN, RP
通讯作者：
MARCINCIN, RP