无线电技术
放射基因组学
生物标志物
成像生物标志物
医学
表型
计算生物学
基因组学
肿瘤科
胶质母细胞瘤
Lasso(编程语言)
生物标志物发现
磁共振成像
生物信息学
病理
生物
蛋白质组学
癌症研究
基因
放射科
计算机科学
遗传学
基因组
万维网
作者
Seung Won Choi,Hwan Ho Cho,Harim Koo,Sang-Ku Park,Karl-Heinz Nenning,Georg Langs,Julia Furtner,Bernhard Baumann,Adelheid Wöehrer,Hee Jin Cho,K. Jason,Doo Sik Kong,Ho Jun Seol,Jung Il Lee,Do Hyun Nam,Hyunjin Park
出处
期刊:Cancers
[MDPI AG]
日期:2020-06-27
卷期号:12 (7): 1707-1707
被引量:17
标识
DOI:10.3390/cancers12071707
摘要
We aimed to evaluate the potential of radiomics as an imaging biomarker for glioblastoma (GBM) patients and explore the molecular rationale behind radiomics using a radio-genomics approach. A total of 144 primary GBM patients were included in this study (training cohort). Using multi-parametric MR images, radiomics features were extracted from multi-habitats of the tumor. We applied Cox-LASSO algorithm to build a survival prediction model, which we validated using an independent validation cohort. GBM patients were consensus clustered to reveal inherent phenotypic subtypes. GBM patients were successfully stratified by the radiomics risk score, a weighted sum of radiomics features, corroborating the potential of radiomics as a prognostic biomarker. Using consensus clustering, we identified three distinct subtypes which significantly differed in the prognosis (“heterogenous enhancing”, “rim-enhancing necrotic”, and “cystic” subtypes). Transcriptomic traits enriched in individual subtypes were in accordance with imaging phenotypes summarized by radiomics. For example, rim-enhancing necrotic subtype was well described by radiomics profiling (T2 autocorrelation and flat shape) and highlighted by the inflammatory genomic signatures, which well correlated to its phenotypic peculiarity (necrosis). This study showed that imaging subtypes derived from radiomics successfully recapitulated the genomic underpinnings of GBMs and thereby confirmed the feasibility of radiomics as an imaging biomarker for GBM patients with comprehensible biologic annotation.
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