Ensemble learning prediction framework for EGFR amplification status of glioma based on terahertz spectral features.
Spectrochim Acta A Mol Biomol Spectrosc
; 316: 124351, 2024 Aug 05.
Article
en En
| MEDLINE
| ID: mdl-38692109
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Espectroscopía de Terahertz
/
Receptores ErbB
/
Glioma
Límite:
Humans
Idioma:
En
Revista:
Spectrochim Acta A Mol Biomol Spectrosc
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2024
Tipo del documento:
Article
País de afiliación:
China