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

Niha Beig, Salendra Singh, Kaustav Bera, Prateek Prasanna, Gagandeep Singh, Jonathan Chen, Anas Saeed Bamashmos, Addison Barnett, Kyle Hunter, Volodymyr Statsevych, Virginia B. Hill, Vinay Varadan, Anant Madabhushi, Manmeet S. Ahluwalia, Pallavi Tiwari*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)251-263
Number of pages13
JournalNeuro-oncology
Volume23
Issue number2
DOIs
StatePublished - Feb 1 2021

Keywords

  • glioblastoma
  • machine learning
  • radiogenomics
  • sexual dimorphism

ASJC Scopus subject areas

  • Oncology
  • Clinical Neurology
  • Cancer Research

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