^(18)F-FET PET/CT双模态影像组学特征对成人胶质瘤病理学分级的非侵入性预测分析  

The non-invasive prediction analysis of radiomic features from ^(18)F-FET PET/CT dual imaging modality for tumor grading in untreated adult gliomas

作　　者：华涛 周维燕 周支瑞 黄琪 李明 朱毓华 管一晖 HUA Tao;ZHOU Weiyan;ZHOU Zhirui;HUANG Qi;LI Ming;ZHU Yuhua;GUAN Yihui(Department of Nuclear Medicine/PET Center,Huashan Hospital,Fudan University,Shanghai 200235,China;Department of Radiotherapy,Huashan Hospital,Fudan University,Shanghai 200040,China)

机构地区：[1]复旦大学附属华山医院核医学科/PET中心,上海200235 [2]复旦大学附属华山医院放射治疗中心,上海200040

出　　处：《肿瘤影像学》2024年第2期127-135,共9页Oncoradiology

基　　金：国家自然科学基金(82302337);上海周良辅医学发展基金会“脑科学与脑疾病青年创新计划”。

摘　　要：目的:基于^(18)F-FET正电子发射体层成像(positron emission tomography,PET)/计算机体层成像(computed tomography,CT)双模态影像组学特征构建模型,并对成人胶质瘤的病理学分级进行非侵入性预测分析。方法:回顾并分析58例经组织病理学检查证实的未治疗成年胶质瘤患者的^(18)F-FET PET/CT影像学数据,根据病理学分级将患者分成低级别胶质瘤组[世界卫生组织(World Health Organization,WHO)Ⅱ级,共计32例]和高级别胶质瘤组(WHOⅢ级13例、WHOⅣ级13例,共计26例)。在PET、CT影像模态中分别提取105个影像组学特征参数进行分析,采用基于R语言机器学习算法的5折交叉验证最小绝对收缩与选择算子(least absolute shrinkage and selection operator,LASSO)回归分析方法,构建成人胶质瘤病理学分级的3个独立的影像组学预测模型(PET-Rad模型、CT-Rad模型和PET/CT-Rad模型),然后再采用全子集回归对影像组学预测模型进行校正。采用受试者工作特征曲线的曲线下面积(area under curve,AUC)对预测模型进行评价。结果:基于4个^(18)F-FET PET影像组学参数建立PET-Rad模型的AUC为0.845(95%CI 0.726~0.927);基于3个CT影像组学参数构建的CT-Rad模型的AUC为0.802(95%CI 0.676~0.895);而联合3个CT和2个PET影像组学特征的^(18)F-FET PET/CT-Rad模型的AUC为0.901(95%CI 0.794~0.964),准确度为86.21%。DeLong检验结果显示PET/CT-Rad模型优于CT-Rad模型(P=0.032)。尽管PET/CT-Rad模型效能优于PET-Rad模型,但差异无统计学意义(P=0.146)。构建胶质瘤病理学级别预测的PET/CT-Rad模型中,3个CT影像组学参数分别为firstorder_10Percentile、glrlm_LowGrayLevelRunEmphasis、ngtdm_Busyness,其中glrlm_LowGrayLevelRunEmphasis是最重要的预测变量,其相对重要性为30.97%;2个PET组学特征为firstorder_Maximum和ngtdm_Contrast,其相对重要性分别为21.99%、21.01%。结论:基于^(18)F-FET PET与CT双模态影像组学特征相结合的预测模型能够有效地预测�Objective:To build radiomics models based on the features from^(18)F-FET positron emission tomography(PET)/computed tomography(CT)dual imaging modality,and investigate the predictive efficacy for tumor grading in untreated adult gliomas.Methods:The^(18)F-FET PET/CT imaging data of 58 histopathologically confirmed untreated adult gliomas were retrospectively analyzed.Based on pathological grading,the patients were divided into low-grade glioma groups[World Health Organization(WHO)GradeⅡ,32 cases in total]and high-grade gliomas(13 cases for WHO gradeⅢ,13 cases for WHO gradeⅣ,26 cases in total).105 radiomics features were extracted from PET and CT modalities respectively.Five-fold cross-validation least absolute shrinkage and selection operator(LASSO)regression analysis based on R-language machine learning algorithm and allsubset regression were adopted to filter and optimize the identify the optimal feature combinations with higher distinguishing power for glioma grading.Three independent radiomics prediction models(PET-Rad model,CT-Rad model and PET/CT-Rad model)for adult glioma pathological grading were constructed.The area under the receiver operating characteristic curve(AUC)was used to evaluate the prediction model.Results:The AUC of PET-Rad model based on four^(18)F-FET PET radiomics features was 0.845(95%CI 0.726-0.927)and the AUC of CT-Rad model consisting of three CT radiomics features was 0.802(95%CI 0.676-0.895).The AUC of the^(18)F-FET PET/CT-Rad model combined with three CT and two PET radiomics features was 0.901(95%CI 0.794-0.964),and the accuracy was 86.21%.DeLong test results showed that PET/CT-Rad model was superior to CT-Rad model(P=0.032).The efficacy of PET/CT-Rad model was better than that of PET-Rad model,but there was no statistical difference(P=0.146).The three CT imaging parameters in PET/CT-Rad model were firstorder_10Percentile,glrlm_LowGrayLevelRunEmphasis,and ngtdm_Busyness,while Glrlm_LowGrayLevelRunEmphasis was the most important predictive variable with a relative importance o

关 键 词：胶质瘤 正电子发射体层成像/计算机体层成像 ^(18)F-FET 影像组学 病理学分级 

分 类 号：R739.41[医药卫生—肿瘤] R445.1[医药卫生—临床医学]