MRI组学特征构建机器学习模型预测胸腰段再骨折  

A machine learning prediction model based on MRI radiomics for refracture of thoracolumbar segments

作　　者：刘进 印宏坤[3] 陈果 张宇 顾祖超[2] 唐静[4] Liu Jin;Yin Hongkun;Chen Guo;Zhang Yu;Gu Zuchao;Tang Jing(Department of Orthopedics,Chengdu Seventh People’s Hospital,Chengdu 610041,Sichuan Province,China;Department of Orthopedics,Chengdu First People’s Hospital,Chengdu 610041,Sichuan Province,China;Beijing Infervision Technology Co.,Ltd.,Beijing 100080,China;Department of Radiology,West China Hospital,Sichuan University,Chengdu 610041,Sichuan Province,China)

机构地区：[1]成都市第七人民医院骨科,四川省成都市610041 [2]成都市第一人民医院骨科,四川省成都市610041 [3]推想医疗科技股份有限公司,北京市100080 [4]四川大学华西医院放射科,四川省成都市610041

出　　处：《中国组织工程研究》2022年第33期5323-5328,共6页Chinese Journal of Tissue Engineering Research

基　　金：四川省卫生健康委员会科研项目(20PJ194),项目负责人:刘进;成都市卫生健康委员会科研项目(2020133),项目负责人:刘进。

摘　　要：背景:影像组学能够对图像异质性进行量化,能否从骨质疏松椎体MRI影像中筛选出类似指纹等具有特征性的影像差异,用于预测再骨折的发生仍有待研究。目的:探讨通过MRI组学特征联合临床信息构建椎体强化后胸腰段再骨折机器学习预测模型的可行性。方法:回顾性收集成都市第一人民医院2014年5月至2019年4月由MRI确诊并行椎体强化治疗的骨质疏松性椎体压缩骨折患者资料,使用PyRadiomics工具提取强化前T_(11)-L_(2)节段非骨折椎体MRI T1序列影像组学特征。所有模型在训练集中构建,并在验证集中进行预测效能评估,采用最小绝对收缩和选择算子对组学数据进行降维,采用逻辑回归、随机森林和自适应提升算法针对临床信息、组学特征和二者结合构建相应的再骨折预测模型,采用受试者工作特征曲线对模型的诊断效能进行评估,采用决策分析曲线比较各模型的临床价值。结果与结论:①共纳入135例患者的336个椎体,其中67个椎体发生再骨折,每个椎体分别提取到1746个组学特征,经降维共获得13个重要特征;②在3种计算方法方案下,综合模型在训练集与验证集中的AUC均显著高于临床模型(P<0.05),决策分析曲线同样显示综合模型预测胸腰段再骨折的净收益在大部分阈值区间内均高于临床模型;③结果表明,采用MRI T1序列影像组学特征联合临床信息构建再骨折预测模型具有可行性,有助于早期识别出具有高度再骨折风险的椎体。BACKGROUND:Radiomics can be used to quantify image heterogeneity.Whether radiomics can be used to screen out features such as fingerprint from MRI images of osteoporotic vertebral bodies to predict the occurrence of new fracture is worth studying.OBJECTIVE:To explore the feasibility of constructing a machine learning prediction model for thoracolumbar refracture after vertebral augmentation through combining MRI radiomics features and clinical information.METHODS:This study retrospectively collected the data of patients who were diagnosed with osteoporotic vertebral compression fracture by MRI and treated with percutaneous vertebral augmentation in Chengdu First People’s Hospital from May 2014 to April 2019.PyRadiomics was used to extract the imaging features of T1 sequences of vertebral MRI at the T_(11)-L_(2) segments before percutaneous vertebral augmentation.All models were constructed in the training set,and prediction performance evaluation was performed in the validation set.Feature dimension reduction was conducted by applying least absolute shrinkage and selection operator regression.The corresponding refracture prediction models were constructed by multivariate logistic regression,random forest and adaptive lifting algorithm analysis using clinical parameters,selected features or the integrating of both.The diagnostic efficacy of the model was evaluated using the receiver operating characteristic curve.The decision analysis curve was used to compare the clinical value of each model.RESULTS AND CONCLUSION:(1)A total of 336 vertebrae were included in 135 patients,of which 67 vertebrae had refractures.1746 features were extracted from each vertebra,and 13 important features were obtained through dimension reduction.(2)Among the three models,area under curve of the combined model in the training set and validation set was significantly higher than that of the clinical model(P<0.05),and the decision analysis curve also showed that the net benefit of the combined model in predicting thoracolumbar refracture

关 键 词：影像组学 骨质疏松 椎体压缩骨折 经皮椎体成形 再骨折 机器学习 预测模型 

分 类 号：R445.2[医药卫生—影像医学与核医学] R319[医药卫生—诊断学]