ArticleNeuro-oncology advances
Radiomics-based preoperative survival prediction in newly diagnosed glioblastoma: A multicohort study with internal and external validation.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Article
- MRI-derived habitat heterogeneity for overall survival risk stratification in IDH-wildtype, CNS WHO grade 4 glioblastoma.Frontiers in cell and developmental biology · 2026Article
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Authors and funding
15 authors.
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Abstract
Background: Preoperative survival prediction in newly diagnosed glioblastoma (nGBM) remains challenging due to limited robustness and standardization across radiomic approaches. We aimed to validate a machine learning-based prognostic model using preoperative MR images and assess its generalizability. Methods: Two independent cohorts were analyzed: the Kansai Molecular Diagnosis Network for CNS Tumors (KNBTG) and The Cancer Genome Atlas (TCGA). All cases with available preoperative MR images (T1WI, T2WI, and Gd-T1WI) were included. The KNBTG cohort was divided into a training dataset (TD, Results: Radiomic high-risk status was associated with significantly shorter OS in both ITD and ETD (log-rank Conclusions: Radiomics-based modeling provides reproducible prognostic value in nGBM. The feature "T2_core_GLCMhomogeniety_3_SD" may serve as a reliable imaging biomarker for preoperative risk stratification and individualized treatment planning.
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