基于多参数MRI构建异柠檬酸脱氢酶野生型胶质母细胞瘤预后模型的预测价值  

Predictive value of a prognostic model for isocitrate dehydrogenase wild-type glioblastoma based on multi-parameter MRI

作　　者：张风江[1] 马草原 马泽宇 王子龙 阎静[2] 郭杨 张振宇[1] Zhang Fengjiang;Ma Caoyuan;Ma Zeyu;Wang Zilong;Yan Jing;Guo Yang;Zhang Zhenyu(Department of Neurosurgery,the First Affiliated Hospital of Zhengzhou University,Zhengzhou 450052,China;Department of Magnetic Resonance,the First Affiliated Hospital of Zhengzhou University,Zhengzhou 450052,China;Department of Neurosurgery,Henan Provincial People′s Hospital,Zhengzhou 450003,China)

机构地区：[1]郑州大学第一附属医院神经外科,郑州450052 [2]郑州大学第一附属医院磁共振科,郑州450052 [3]河南省人民医院神经外科,郑州450003

出　　处：《中华神经外科杂志》2024年第7期686-693,共8页Chinese Journal of Neurosurgery

基　　金：国家自然科学基金(82273493,82102149,82173090);河南省科技攻关项目(202102310138)。

摘　　要：目的基于多参数MRI的影像组学特征构建预测异柠檬酸脱氢酶(IDH)野生型胶质母细胞瘤(GBM)患者预后的模型并予以验证。方法回顾性分析郑州大学第一附属医院神经外科2018年10月至2020年12月期间(数据集1,172例)及河南省人民医院神经外科2011年1月至2021年9月期间(数据集2,89例)行肿瘤切除术,且术后病理学诊断为成人型IDH野生型GBM患者的临床和MRI资料。将多个MRI序列(包括T1加权成像、钆剂对比增强后T1加权成像、T2加权成像、液体衰减反转恢复序列、弥散加权成像及其表观弥散系数)进行预处理后,将数据集1按1∶1的比例分为训练集和内部验证集,数据集2作为外部验证集,采用单因素Cox比例风险回归模型与Lasso-cox分析结合的方法筛选影像组学特征,并结合临床危险因素构建预测GBM患者预后的临床-影像组学模型。采用一致性指数、赤池信息量准则(AIC)、综合判别改善(IDI)指数、Kaplan-Meier生存曲线、诺莫图、校准曲线以及决策曲线分析评估临床-影像组学模型的预测效能,并与仅采用临床危险因素构建的临床模型进行比较。结果共筛选出18个与IDH野生型GBM患者预后显著相关的影像组学特征并建立影像组学预后模型(Radscore);Kaplan-Meier生存曲线显示,训练集、内部验证集和外部验证集基于Radscore划分的高、低风险组间预后的差异均有统计学意义(均P<0.05);单因素及多因素Cox比例风险回归分析显示,Radscore在训练集中为独立预后因素(P<0.05)。与单纯的临床模型相比,Radscore的加入使预测模型在训练集、内部验证集、外部验证集中的一致性指数由0.682、0.682、0.791分别提升至0.785、0.694、0.823,AIC分别由727.872、703.796、666.732降至683.771、697.790、654.837;临床-影像组学模型在训练集、内部验证集、外部验证集中的IDI指数分别为0.268、0.051、0.100(均P<0.05),诺莫图及其校准曲线均表现出�Objective To construct and validate a prognostic model for predicting the prognosis of isocitrate dehydrogenase(IDH)wild-type glioblastoma(GBM)patients based on the radiomic features of multiparametric MRI.Methods A retrospective analysis was conducted on clinical information and MRI data from patients pathologically diagnosed with adult-type IDH wild-type GBM patients who underwent tumor resection surgery at the Department of Neurosurgery,the First Affiliated Hospital of Zhengzhou University(dataset 1,n=172 cases,from October 2018 to December 2020),and at the Department of Neurosurgery,Henan Provincial People′s Hospital(dataset 2,n=89 cases,from January 2011 to September 2021).After preprocessing multiple MRI sequences,including T1-weighted imaging,gadolinium-enhanced T1-weighted imaging,T2-weighted imaging,fluid-attenuated inversion recovery sequences,diffusion-weighted imaging,and apparent diffusion coefficient maps,dataset 1 was divided into a training set and an internal validation set at a ratio of 1∶1.Dataset 2 was used as an external validation set.A univariate Cox proportional hazards regression model combined with Lasso-cox analysis was employed to select radiomic features.These radiomic features were integrated with clinical risk factors to develop a clinic-radiomic model for predicting the prognosis of GBM patients.The predictive performance of the model was evaluated using the concordance index,Akaike information criterion(AIC),integrated discrimination improvement(IDI),Kaplan-Meier survival curves,nomograms,calibration curves,and decision curves and then was compared with that of the model established using clinical risk factors.Results Eighteen radiomic features significantly associated with the prognosis of IDH wild-type GBM patients were selected to construct a radiomic model(Radscore).Kaplan-Meier survival curves demonstrated statistically significant prognostic differences between high-risk and low-risk groups stratified by Radscore in the training set,internal validation set,and external v

关 键 词：胶质母细胞瘤 预后 机器学习 影像组学 预测 

分 类 号：R739.4[医药卫生—肿瘤]