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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:

42

期数:

2025年第9期

页码:

1147-1152

栏目:

医学影像物理

出版日期:

2025-09-30

文章信息/Info

Title:

Prognostic prediction of patients in vegetative state based on quantitative analysis of diffusion tensor imaging

文章编号:

1005-202X（2025）09-1147-06

作者:

叶思敏1; 钟海鹂2; 梁杞梅3; 黄曦妍2; 王思训1; 黄靖1

1.南方医科大学生物医学工程学院， 广东 广州 510515； 2.南方医科大学珠江医院康复医学科， 广东 广州 510282； 3.南方医科大学第十附属医院（东莞市人民医院）康复医学科， 广东 东莞 523000

Author(s):

YE Simin1;  ZHONG Haili2;  LIANG Qimei3;  HUANG Xiyan2;  WANG Sixun1;  HUANG Jing1

1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China 2. Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou 510282, China 3. Department of Rehabilitation Medicine, the Tenth Affiliated Hospital of Southern Medical University (Dongguan Peoples Hospital), Dongguan 523000, China

关键词:

植物状态; 分数各向异性; 支持向量机; 扩散张量成像

Keywords:

Keywords: vegetative state fractional anisotropy support vector machine diffusion tensor imaging

分类号:

R318；R445.2

DOI:

DOI:10.3969/j.issn.1005-202X.2025.09.004

文献标志码:

A

摘要:

目的：探讨预后不同的植物状态（VS）患者脑白质纤维束的结构完整性差异，构建预后预测模型，以在疾病稳定后早期预测患者1年后的预后。方法：回顾性分析南方医科大学珠江医院康复医学科收治的52例VS患者，根据1年随访的修订版昏迷恢复量表（CRS-R）评分将患者分为预后良好组（n=22）和预后不良组（n=30）。采用扩散张量成像技术提取患者脑白质纤维束的各向异性分数（FA），首次将CRS-R的视觉评分与FA值结合作为模型的输入特征。为优化模型构建，采用LASSO筛选特征，并运用合成少数类过采样技术进行数据平衡处理，最终基于支持向量机（SVM）算法，采用留一交叉验证构建预后预测模型，并通过综合评估受试者工作特征曲线下面积（AUC）、灵敏度、准确率、特异性和F1分数等指标全面评估模型效能。结果：经LASSO特征筛选后，脑桥横束、内侧丘系、绒毡层、胼胝体压部和视觉评分被确定为关键预测指标，基于上述特征构建的多模态SVM预测模型可有效预测VS患者的1年预后，其预测效能达到较高水平（AUC=0.894）。结论：结合特定白质纤维束FA值与视觉评分的SVM模型对预测VS患者1年后预后具有较好的预测效能。

Abstract:

Abstract: Objective To analyze the differences in structural integrity of cerebral white matter fiber bundles in vegetative state (VS) patients with different prognoses, and to construct an early-stage prognostic prediction model for 1-year post-stabilization prognosis. Methods A retrospective analysis was conducted on 52 VS patients admitted to the Department of Rehabilitation Medicine at Zhujiang Hospital of Southern Medical University. Patients were stratified into good prognosis (n=22) and poor prognosis (n=30) at 1-year follow-up based on Coma Recovery Scale-Revised (CRS-R) scores. The fractional anisotropy values of cerebral white matter fiber bundles were derived from diffusion tensor imaging, and for the first time, the scores of the visual subscales of CRS-R were combined with FA values as input features for the prognostic model. To optimize model construction, the least absolute shrinkage and selection operator regression was employed for feature screening, and synthetic minority over-sampling technique for data balancing. The prognostic prediction model was subsequently developed using a ?upport vector machine algorithm and validated via ?eave-one-out cross-validation. Model performance was evaluated using area under receiver operating characteristic curve, along with accuracy, sensitivity, specificity, and F1 score metrics. Results Following LASSO regression feature screening, the pontine crossing tract, medial lemniscus, tapetum, splenium of corpus callosum, and visual subscale scores were identified as key predictors. A multimodal SVM-based prediction model constructed with the above features could effectively predict the 1-year prognosis of VS patients, achieving a high predictive performance (AUC=0.894). Conclusion The SVM-based model integrating FA values of specific white matter fiber bundles and visual subscale scores demonstrates excellent predictive performance in predicting the 1-year prognosis of VS patients.

备注/Memo

备注/Memo:

【收稿日期】2025-05-20 【基金项目】国家自然科学基金（82171174, 82371184） 【作者简介】叶思敏，硕士研究生，研究方向：慢性意识障碍患者的脑损伤与预后，E-mail: 1737481963@qq.com 【通信作者】黄靖，博士，副教授，研究方向：模式识别、意识障碍患者脑龄与脑损伤，E-mail: jhuangyg@smu.edu.cn

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更新日期/Last Update: 2025-09-30