Abstract
Glioblastoma (GBM) is a highly aggressive brain tumor with poor outcomes and limited treatment options. The telomerase reverse transcriptase (TERT) promoter mutation, one of the key biomarkers in GBM, is linked to tumor progression and prognosis. This study employed terahertz time-domain spectroscopy (THz-TDS) to analyze frozen GBM tissue sections, extracting six spectral features: absorption coefficient, dielectric loss factor, dielectric constant, extinction coefficient, refractive index, and dielectric loss tangent. LASSO regression was employed for feature selection, and then principal component analysis (PCA) was applied to minimize inter-feature correlations. A Random Forest classifier built on these features successfully predicted TERT mutation status, achieving an area under the receiver operating characteristic curve (AUC) of 0.908 in the validation set. Our findings demonstrate that THz spectroscopy, coupled with machine learning, can identify molecular differences associated with TERT mutations, supporting its potential as a rapid, intraoperative diagnostic tool for personalized GBM treatment. This approach could enhance surgical decision-making and optimize patient outcomes through precise, real-time molecular diagnostics.