Abstract：
Epidermal growth factor receptor (EGFR) plays a pivotal role in the initiation and progression of gliomas. In particular, in glioblastoma, EGFR amplification emerges as a catalyst for invasion, proliferation, and resistance to radiotherapy and chemotherapy. Current approaches are not capable of providing rapid diagnostic results of molecular pathology. In this study, we propose a terahertz spectroscopic approach for predicting the EGFR amplification status of gliomas for the first time. A machine learning model was constructed using the terahertz response of the measured glioma tissues, including the absorption coefficient, refractive index, and dielectric loss tangent. The novelty of our model is the integration of three classical base classifiers, i.e., support vector machine, random forest, and extreme gradient boosting. The ensemble learning method combines the advantages of various base classifiers, this model has more generalization ability. The effectiveness of the proposed method was validated by applying an individual test set. The optimal performance of the integrated algorithm was verified with an area under the curve (AUC) maximum of 85.8 %. This signifies a significant stride toward more effective and rapid diagnostic tools for guiding postoperative therapy in gliomas.
摘要：
表皮生长因子受体（EGFR）在胶质瘤的发生和发展中起关键作用。特别是,在胶质母细胞瘤中，EGFR扩增成为入侵的催化剂，扩散,以及对放疗和化疗的抵抗力。目前的方法不能提供分子病理学的快速诊断结果。在这项研究中,我们首次提出了一种预测胶质瘤EGFR扩增状态的太赫兹光谱方法。利用测得的神经胶质瘤组织的太赫兹响应构建了机器学习模型,包括吸收系数，折射率,和介电损耗角正切。我们模型的新颖之处在于集成了三个经典的基分类器，即，支持向量机，随机森林,和极端梯度提升。集成学习方法结合了各种基分类器的优点，该模型具有更强的泛化能力。通过应用单个测试集验证了该方法的有效性。曲线下面积（AUC）最大值为85.8％，验证了集成算法的最佳性能。这标志着朝着更有效，更快速的诊断工具迈出了重要的一步，以指导神经胶质瘤的术后治疗。
