ArticleNeuro-oncology2021
Sexually dimorphic radiogenomic models identify distinct imaging and biological pathways that are prognostic of overall survival in glioblastoma.
Article in Neuro-oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 38 citations in OpenAlex.
- Deciphering the glioblastoma microenvironmental landscape with multi-modal radiogenomics to guide prognosis and personalized therapy.Journal of translational medicine · 2026Article
- Federated Learning in Healthcare: From Research to Real-World Deployment.Annual review of biomedical engineering · 2026Review
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Radiomics and comparative neuroimaging of canine gliomas and human glioblastomas: a step toward precision medicine.Frontiers in veterinary science · 2026Article
- Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Patients With Glioblastoma.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025Review
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.The Lancet. Oncology · 2024Review
- Sex-Specific Differences in Patients withAJNR. American journal of neuroradiology · 2024Article
- Radiogenomic profiling of global DNA methylation associated with molecular phenotypes and immune features in glioma.BMC medicine · 2024Article
- Review
- Towards equitable AI in oncology.Nature reviews. Clinical oncology · 2024Review
- Current approaches in glioblastoma multiforme immunotherapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Review
- Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases.Journal of neuro-oncology · 2024Article
- Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning.European radiology · 2024Article
- Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding.Frontiers in neuroscience · 2024Article
- Segmentation of glioblastomas via 3D FusionNet.Frontiers in oncology · 2024Article
- Sex-specific molecular differences in glioblastoma: assessing the clinical significance of genetic variants.Frontiers in oncology · 2023Review
- Imaging of GBM in the Age of Molecular Markers and MRI Guided Adaptive Radiation Therapy.Journal of clinical medicine · 2022Review
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Authors and funding
15 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundRecent 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.
methodsIn 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.
resultsCox 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.
conclusionsSexually dimorphic radiogenomic models could help risk-stratify GBM patients for personalized treatment decisions.
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