Sun Zhiyan, Du Minghui, Wu Xianhao, Tao Rui, Sun Peiyuan, Zheng Shaowen, Zhang Zhaohui, Zhou Dabiao, Zhao Xiaoyan, Yang Pei
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Sci Rep. 2025 May 27;15(1):18480. doi: 10.1038/s41598-025-03161-x.
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.
胶质母细胞瘤（GBM）是一种侵袭性很强的脑肿瘤，预后较差且治疗选择有限。端粒酶逆转录酶（TERT）启动子突变是GBM的关键生物标志物之一，与肿瘤进展和预后相关。本研究采用太赫兹时域光谱（THz-TDS）分析冷冻的GBM组织切片，提取了六个光谱特征：吸收系数、介电损耗因子、介电常数、消光系数、折射率和介电损耗角正切。采用LASSO回归进行特征选择，然后应用主成分分析（PCA）来最小化特征间的相关性。基于这些特征构建的随机森林分类器成功预测了TERT突变状态，在验证集中的受试者工作特征曲线下面积（AUC）达到0.908。我们的研究结果表明，太赫兹光谱结合机器学习可以识别与TERT突变相关的分子差异，支持其作为一种快速的术中诊断工具用于个性化GBM治疗的潜力。这种方法可以通过精确的实时分子诊断来加强手术决策并优化患者预后。
