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
Department of Radiology, University of California at San Diego, San Diego, CA 92121, USA.
Research, Radiology, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA 92161, USA.
Diagnostics (Basel). 2022 Apr 14;12(4):987. doi: 10.3390/diagnostics12040987.
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.
爆炸相关的轻度创伤性脑损伤（bmTBI）常导致长期后遗症，但由于对主要病理生理学了解不足，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）模型应用于扩散张量成像（DTI）数据集，以阐明区分bmTBI与健康对照（HC）的白质特征，从而构建一个供未来验证的诊断模型。20名亚急性/慢性bmTBI患者和19名曾部署到战场的HC人员接受了DTI检查。使用基于纤维束的分析来选择用于建模的临床相关特征，该分析在五个DTI指标中识别出整个白质纤维束中的组间差异，以阐明损伤的发病机制。然后使用具有交叉验证的SVM模型对这些特征进行分析。基于纤维束的分析显示，bmTBI组的径向扩散率（RD）异常降低，分数各向异性（FA）以及轴向/径向扩散率比值（AD/RD）升高，主要出现在前纤维束（29个特征）。SVM模型显示，放射冠前/上部的FA以及胼胝体和内囊前肢的AD/RD（5个特征）最能区分bmTBI与HC，准确率达89%。这是首次将SVM仅基于DTI指标应用于明确纤维束中识别bmTBI的突出特征，若成功验证，可促进针对性的治疗干预。
