Histopathology-correlated evaluation of magnetic resonance spectroscopy for high-grade glioma prediction: A small-scale neuro-oncology study with exploratory machine learning

Background

High-grade gliomas are aggressive primary brain tumours that require timely and accurate preoperative assessment. Magnetic resonance spectroscopy provides non-invasive metabolic information that may support neuro-oncological lesion characterisation. However, its tendency to show good sensitivity but limited specificity, particularly in the presence of metastasis, abscess, and other intracranial mimics, is already recognised. The clinical value of small, fully histopathology-confirmed cohorts lies in clarifying how these limitations appear in real diagnostic practice.