依据MRI影像组学数据构建颈椎失稳诊断分类模型的可行性  

Feasibility of constructing a diagnostic classification model for cervical instability by magnetic resonance imaging radiomics

作　　者：路广琦 崔莹[1] 李靖 俞张镜泽 朱立国[1] 于杰[1] 庄明辉 Lu Guangqi;Cui Ying;Li Jing;Yu Zhangjingze;Zhu Liguo;Yu Jie;Zhuang Minghui(Wangjing Hospital,China Academy of Chinese Medical Sciences,Beijing 100102,China)

机构地区：[1]中国中医科学院望京医院,北京市100102

出　　处：《中国组织工程研究》2024年第33期5370-5374,共5页Chinese Journal of Tissue Engineering Research

基　　金：北京市科技计划首都临床诊疗技术研究及转化应用项目(Z211100002921023),项目负责人:于杰;国家自然科学基金面上项目(82074455),项目负责人:于杰;中国中医科学院科技创新工程重大攻关项目(CI2021A02002),项目负责人:于杰。

摘　　要：背景:既往颈椎失稳相关研究在传统影像学检查下对其病变发展过程中的动静力交互作用关系和病理特征变化阐释不明,近年来逐渐兴起的MRI影像组学可为颈椎失稳的深入研究提供新的途径。目的:探讨MRI影像组学在颈椎失稳研究中的应用价值。方法:通过招募广告和中国中医科学院望京医院脊柱二科门诊,纳入18-45岁青年颈椎失稳受试者和非失稳受试者进行颈椎MRI采集,在椎间盘所在的横断面图层,对椎间盘区、关节突区、椎前肌区、颈后肌群深层区及颈后肌群浅层区等5个特定感兴趣区域进行手动分割,以提取并筛选影像组学特征,最终进行颈椎失稳诊断分类模型的构建,并使用曲线下面积评价模型的效能。结果与结论:①共纳入56例颈椎失稳受试者和55例非失稳受试者,每个感兴趣区域提取影像组学特征各1688个;经过筛选最终获得300组具有特异性的影像组学特征组合,每个感兴趣区域各60组;②初步建立了5个感兴趣区域的颈椎失稳诊断分类模型,其中关节突区的支持向量机模型和颈后肌群深层区的支持向量机模型对于失稳和非失稳的分类具有一定的准确性,十折交叉验证平均曲线下面积分别为0.7197和0.7033;③椎间盘区的Logistic模型、椎前肌区的LightGBM模型及颈后肌群浅层区的Logistic模型对于失稳和非失稳的分类准确性一般,十折交叉验证平均曲线下面积分别为0.6504,0.6207和0.6442;④证明了MRI影像组学在颈椎失稳研究中应用的可行性,进一步深化了对颈椎失稳发病机制的认识,同时也为颈椎失稳的精准诊断提供了客观依据来源。BACKGROUND:Previous studies on cervical instability failed to explain the dynamic and static interaction relationship and pathological characteristics changes in the development of cervical lesions under the traditional imaging examination.In recent years,the emerging nuclear magnetic resonance imaging(MRI)radiomics can provide a new way for in-depth research on cervical instability.OBJECTIVE:To investigate the application value of MRI radiomics in the study of cervical instability.METHODS:Through recruitment advertisements and the Second Department of Spine of Wangjing Hospital,China Academy of Chinese Medical Sciences,young cervical vertebra unstable subjects and non-unstable subjects aged 18-45 years were included in the cervical vertebra nuclear magnetic image collection.Five specific regions of interest,including the intervertebral disc region,the facet region,the prevertebral muscle region,the deep region of the posterior cervical muscle group,and the superficial region of the posterior cervical muscle group,were manually segmented to extract and screen the image features.Finally,the cervical instability diagnosis classification model was constructed,and the effectiveness of the model was evaluated using the area under the curve.RESULTS AND CONCLUSION:(1)A total of 56 subjects with cervical instability and 55 subjects with non-instability were included,and 1688 imaging features were extracted for each region of interest.After screening,300 sets of specific image feature combinations were obtained,with 60 sets of regions of interest for each group.(2)Five regions of interest diagnostic classification models for cervical instability were initially established.Among them,the support vector machine model for the articular process region and the support vector machine model for the deep cervical muscle group had certain accuracy for the classification of instability and non-instability,and the average area under the curve of ten-fold cross-validation was 0.7197 and 0.7033,respectively.(3)The Logistic model in th

关 键 词：颈椎失稳 MRI影像组学 诊断分类模型 感兴趣区域 曲线下面积 

分 类 号：R459.9[医药卫生—治疗学] R318[医药卫生—临床医学] R681.5