Sexually dimorphic radiogenomic models identify distinct imaging and biological pathways that are prognostic of overall survival in glioblastoma

被引:33
作者
Beig, Niha [1 ]
Singh, Salendra [1 ]
Bera, Kaustav [1 ]
Prasanna, Prateek [2 ]
Singh, Gagandeep [3 ]
Chen, Jonathan [1 ]
Bamashmos, Anas Saeed [4 ]
Barnett, Addison [4 ]
Hunter, Kyle [4 ]
Statsevych, Volodymyr [4 ]
Hill, Virginia B. [5 ]
Varadan, Vinay [1 ]
Madabhushi, Anant [1 ,6 ]
Ahluwalia, Manmeet S. [4 ]
Tiwari, Pallavi [1 ]
机构
[1] Case Western Reserve Univ, Cleveland, OH 44106 USA
[2] SUNY Stony Brook, Dept Biomed Informat, Stony Brook, NY 11794 USA
[3] Newark Beth Israel Med Ctr, Dept Radiol, Newark, NJ USA
[4] Cleveland Clin, Brain Tumor & Neurooncol Ctr, Cleveland, OH 44106 USA
[5] Northwestern Univ, Feinberg Sch Med, Dept Radiol, Sect Neuroradiol, Chicago, IL 60611 USA
[6] Louis Stokes Cleveland Vet Adm Med Ctr, Cleveland, OH USA
基金
美国国家卫生研究院;
关键词
glioblastoma; machine learning; radiogenomics; sexual dimorphism;
D O I
10.1093/neuonc/noaa231
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background. Recent epidemiological studies have suggested that sexual dimorphism influences treatment response and prognostic outcome in glioblastoma (GBM). To this end, we sought to (i) identify distinct sex-specific radiomic phenotypes-from tumor subcompartments (peritumoral edema, enhancing tumor, and necrotic core) using pretreatment MRI scans-that are prognostic of overall survival (OS) in GBMs, and (ii) investigate radiogenomic associations of the MRI-based phenotypes with corresponding transcriptomic data, to identify the signaling pathways that drive sex-specific tumor biology and treatment response in GBM. Methods. In a retrospective setting, 313 GBM patients (male = 196, female = 117) were curated from multiple institutions for radiomic analysis, where 130 were used for training and independently validated on a cohort of 183 patients. For the radiogenomic analysis, 147 GBM patients (male = 94, female = 53) were used, with 125 patients in training and 22 cases for independent validation. Results. Cox regression models of radiomic features from gadolinium T1-weighted MRI allowed for developing more precise prognostic models, when trained separately on male and female cohorts. Our radiogenomic analysis revealed higher expression of Laws energy features that capture spots and ripple-like patterns (representative of increased heterogeneity) from the enhancing tumor region, as well as aggressive biological processes of cell adhesion and angiogenesis to be more enriched in the "high-risk" group of poor OS in the male population. In contrast, higher expressions of Laws energy features (which detect levels and edges) from the necrotic core with significant involvement of immune related signaling pathways was observed in the "low-risk" group of the female population. Conclusions. Sexually dimorphic radiogenomic models could help risk-stratify GBM patients for personalized treatment decisions.
引用
收藏
页码:251 / 263
页数:13
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