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Related Experiment Video

Updated: Jul 9, 2026

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Published on: May 31, 2020

From Anatomy to Outcome: Linking Glioma Location Patterns to Survival Using Non-Negative Matrix Factorization.

Marianne Schell^1,2, Joel Kohler^1,2, Martha Folty-Dumitru^3,4

¹Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany.

Clinical Neuroradiology

|March 27, 2026

Summary

Spatial patterns in IDH-wildtype gliomas provide prognostic value beyond standard markers. Analyzing tumor location using non-negative matrix factorization (NMF) improves survival prediction for glioma patients.

Keywords:

Brain Cox regression Glioma Imaging biomarkers Survival prediction Tumor location

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Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

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Last Updated: Jul 9, 2026

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Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Published on: September 13, 2022

Area of Science:

Neuro-oncology
Radiomics
Computational pathology

Background:

Glioma anatomical distribution may offer prognostic insights.
Conventional markers may not fully capture glioma prognosis.
IDH-wildtype gliomas represent a significant subset requiring refined prognostic tools.

Purpose of the Study:

To determine if data-driven spatial patterns of IDH-wildtype glioma involvement offer prognostic value.
To assess if spatial tumor patterns provide information beyond established clinical and molecular factors.
To investigate the utility of continuous spatial phenotyping in glioma prognosis.

Main Methods:

Applied non-negative matrix factorization (NMF) to preoperative segmentations of 429 IDH-wildtype gliomas.
Extracted six reproducible spatial signatures of tumor involvement.
Integrated spatial patterns into Cox proportional hazards models with clinical and molecular factors (e.g., tumor volume, age, MGMT methylation, ECOG status, extent of resection).

Main Results:

All six spatial patterns significantly correlated with overall survival (p ≤ 0.005).
Left parietal-occipital region involvement was associated with poorer outcomes; temporal patterns showed weaker survival associations.
Incorporating spatial patterns improved model discrimination and revealed interactions between tumor location and burden, altering the impact of tumor volume.

Conclusions:

Continuous spatial phenotyping of IDH-wildtype gliomas using NMF captures prognostically relevant information.
Spatial patterns offer value beyond conventional prognostic markers.
Integrating spatial analyses into radiologic workflows can enhance glioma risk stratification.