Robust Computational Extraction of Non-Enhancing Hypercellular Tumor Regions from Clinical Imaging Data

TL;DR

This paper introduces a robust computational method that accurately maps non-enhancing hypercellular tumor regions from routine MRI scans, aiding clinical decision-making and advancing personalized treatment in neuro-oncology.

Contribution

The study presents a novel framework that combines multiple neural network architectures to reliably identify NEH tumor regions from MRI data, validated against clinical markers.

Findings

Validated against clinical markers like rCBV and ETRL

Demonstrated robustness across different MRI datasets

Supports integration of NEH mapping into clinical workflows

Abstract

Accurate identification of non-enhancing hypercellular (NEH) tumor regions is an unmet need in neuro-oncological imaging, with significant implications for patient management and treatment planning. We present a robust computational framework that generates probability maps of NEH regions from routine MRI data, leveraging multiple network architectures to address the inherent variability and lack of clear imaging boundaries. Our approach was validated against independent clinical markers -- relative cerebral blood volume (rCBV) and enhancing tumor recurrence location (ETRL) -- demonstrating both methodological robustness and biological relevance. This framework enables reliable, non-invasive mapping of NEH tumor compartments, supporting their integration as imaging biomarkers in clinical workflows and advancing precision oncology for brain tumor patients.