摘要
目的 探讨基于影像组学和深度学习特征评分(radiomics-deep learning score, RD-score)联合血液炎性指标构建的列线图在术前预测胶质瘤预后的价值。材料与方法 回顾性分析166例临床确诊胶质瘤的患者病例,按8∶2随机分为训练集(133例)和验证集(33例)。收集患者的临床、血液炎性指标资料,构建组合变量系统性炎症指数(systemic immune inflammation index, SII)、全身炎症反应指数(system inflammation response index, SIRI)、衍生中性粒细胞与淋巴细胞比值(derived neutrophil-to-lymphocyte ratio, dNLR)、中性粒细胞与淋巴细胞比值(neutrophil-to-lymphocyte ratio, NLR)、单核细胞与淋巴细胞比值(monocyte-to-lymphocyte ratio, MLR)、血小板与淋巴细胞比值(platelet-to-lymphocyte ratio, PLR)并计算其截断值。勾画胶质瘤感兴趣体积(volume of interest, VOI)并提取影像组学及深度学习特征,利用最小绝对值收敛和选择算子(least absolute shrinkage and selection operator, LASSO)-Cox进行特征筛选,构建基于影像组学特征评分(radiomics-score,Rad-score)、基于深度学习特征评分(deep learning-score, DL-score)以及RD-score模型,并比较三者的受试者工作特征曲线下面积(area under the curve, AUC)以评估其预测效能;基于RD-score对胶质瘤患者进行危险分层,并通过Kaplan-Meier生存分析绘制生存曲线。结合患者的临床因素、血液炎性指标和RD-score,使用多因素Cox回归构建术前预测总生存期(overall survival, OS)的RD-score模型、临床血液学模型和联合模型,计算AUC以评估各模型预测胶质瘤1、3、5年生存率的效能。绘制联合模型列线图,采用C指数(C-index)、校准曲线及决策曲线分析(decision curve analysis, DCA)评估列线图效能。结果 最终筛选出10个组学特征和8个深度学习特征用以构建RD-score。RD-score的预测效能高于Rad-score及DL-score(DeLong检验,P<0.05),根据RD-score可将胶质瘤分为高风险组(RD-score≥1
Objective:To explore the value of a nomogram constructed by integrating radiomics and deep learning-based score(RD-score)with hematological inflammatory markers in preoperatively predicting the prognosis of glioma.Materials and Methods:A total of 166 clinically diagnosed glioma patients were retrospectively enrolled and randomly divided into a training set(133 cases)and a validation set(33 cases)in an 8:2 ratio.Clinical and hematological inflammatory marker of the patients were collected.Composite variables,including systemic immune inflammation index(SII),systemic immune response index(SIRI),derived neutrophil-to-lymphocyte ratio(dNLR),neutrophil-to-lymphocyte ratio(NLR),monocyte-to-lymphocyte ratio(MLR),and platelet-to-lymphocyte ratio(PLR)were constructed,and their optimal cut-off values were calculated.Delineating the volume of interest(VOI)for gliomas and extracting radiomics and deep learning features,utilizing least absolute shrinkage and selection operator(LASSO)-Cox for feature selection.Constructing radiomics-score(Rad-score),deep learning-score(DL-score),and RD-score,and comparing their receiver operating characteristic area under the curve(AUC)to assess predictive performance.Kaplan-Meier survival analysis was used to stratify glioma patients based on their RD-score.Integrating clinical data,hematological inflammatory marker,and RD-score,employing multivariable Cox regression to build RD-score model,clinical hematology model,and joint model to predict overall survival(OS).Calculating AUC to evaluate the efficiency of each model in predicting glioma 1,3,and 5-year survival rates.Drawing joint model nomogram and assessing their performance using C-index,calibration curves,and decision curve analysis(DCA).Results:After feature selection,10 radiomics features and 8 deep learning features were selected.The predictive performance of RD-score surpassed that of Rad-score and DL-score(DeLong test,P<0.05).The constructed RD-score divided gliomas into high-risk group(RD-score≥1.09)and low-risk group(RD-score<1
作者
赵杉
阎子康
杨骏骏
张文韬
潘世娇
徐胜生
ZHAO Shan;YAN Zikang;YANG Junjun;ZHANG Wentao;PAN Shijiao;XU Shengsheng(Department of Radiology,the First Affiliated Hospital of Chongqing Medical University,Chongqing 400016,China;Department of Bioinformatics,the Basic Medical School of Chongqing Medical University,Chongqing 400016,China;Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education,Chongqing University,Chongqing 400044,China)
出处
《磁共振成像》
CAS
CSCD
北大核心
2024年第1期88-94,100,共8页
Chinese Journal of Magnetic Resonance Imaging
关键词
胶质瘤
磁共振成像
影像组学
深度学习
机器学习
预后
血液炎性指标
glioma
magnetic resonance imaging
radiomics
deep learning
machine learning
prognosis
hematological inflammatory markers
