Integration of Radiomic and Multi-omic Analyses Predicts Survival of Newly Diagnosed IDH1 Wild-Type Glioblastoma
Integration of Radiomic and Multi-omic Analyses Predicts Survival of Newly Diagnosed IDH1 Wild-Type Glioblastoma
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DOI：
10.3390/cancers11081148
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发表时间：
2019-08-01
期刊：
影响因子：
5.2
通讯作者：
Abdulkarim, Bassam
中科院分区：
文献类型：
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作者：
Chaddad, Ahmad;Daniel, Paul;Abdulkarim, Bassam
来自基因甲基化、突变或表达的患者预后预测因子在IDH 1野生型胶质母细胞瘤（GBM）中受到严重限制。放射组学提供了对肿瘤特征的另一种见解，可以为预测模型提供补充信息。该研究旨在评估将放射组学、基因和临床（多组学）特征整合在一起的预测模型是否能够提高预测患者结局的能力。在本研究中使用了包含200名IDH 1野生型GBM患者的数据集，这些患者来自癌症成像档案（TCIA）（n = 71）和麦吉尔大学健康中心（n = 129）。从肿瘤体积中提取放射组学特征（n = 45），然后将其与生物学变量和临床结果相关联。通过进行10倍交叉验证（n = 200）并利用独立的训练/测试数据集（n = 100/100），从多组学特征导出整合模型并评估预测强度。使用放射组学（方差平方和、大区域/低灰度强调、自相关）、临床（治疗类型、年龄）、遗传（CIC、PIK 3R 1、FUBP 1）和蛋白质表达（p53、波形蛋白）的有限面板的综合模型产生的最大AUC为78.24%（p = 2.9 x 10（-5））。我们认为，使用上述有限的“组学”特征集的多组学模型可以提高预测IDH 1野生型GBM患者结果的能力。
Predictors of patient outcome derived from gene methylation, mutation, or expression are severely limited in IDH1 wild-type glioblastoma (GBM). Radiomics offers an alternative insight into tumor characteristics which can provide complementary information for predictive models. The study aimed to evaluate whether predictive models which integrate radiomic, gene, and clinical (multi-omic) features together offer an increased capacity to predict patient outcome. A dataset comprising 200 IDH1 wild-type GBM patients, derived from The Cancer Imaging Archive (TCIA) (n = 71) and the McGill University Health Centre (n = 129), was used in this study. Radiomic features (n = 45) were extracted from tumor volumes then correlated to biological variables and clinical outcomes. By performing 10-fold cross-validation (n = 200) and utilizing independent training/testing datasets (n = 100/100), an integrative model was derived from multi-omic features and evaluated for predictive strength. Integrative models using a limited panel of radiomic (sum of squares variance, large zone/low gray emphasis, autocorrelation), clinical (therapy type, age), genetic (CIC, PIK3R1, FUBP1) and protein expression (p53, vimentin) yielded a maximal AUC of 78.24% (p = 2.9 x 10(-5)). We posit that multi-omic models using the limited set of 'omic' features outlined above can improve capacity to predict the outcome for IDH1 wild-type GBM patients.