Publications by authors named "Chuyun Tang"
Article Synopsis
- The study aims to create a machine learning nomogram that helps differentiate between two types of brain tumors: supratentorial extraventricular ependymoma (STEE) and supratentorial glioblastoma (GBM).
- Researchers analyzed MRI data from 140 patients and tested various machine learning algorithms, finding that the TreeBagger algorithm provided the best results for tumor classification.
- The developed nomogram, which combines the rad-score from the best algorithm and clinical predictors, showed strong accuracy in distinguishing the tumors, making it a potentially useful tool for clinicians.
Objective: Telomerase reverse transcriptase () promoter mutation status in gliomas is a key determinant of treatment strategy and prognosis. This study aimed to analyze the radiogenomic features and construct radiogenomic models utilizing medical imaging techniques to predict the promoter mutation status in gliomas.
Methods: This was a retrospective study of 304 patients with gliomas.
Background And Purpose: The presence of TERT promoter mutations has been associated with worse prognosis and resistance to therapy for patients with glioblastoma (GBM). This study aimed to determine whether the combination model of different feature selections and classification algorithms based on multiparameter MRI can be used to predict TERT subtype in GBM patients.
Methods: A total of 143 patients were included in our retrospective study, and 2553 features were obtained.
Objective: To investigate the clinical utility of multi-parameter MRI-based radiomics nomogram for predicting telomerase reverse transcriptase (TERT) promoter mutation status and prognosis in adult glioblastoma (GBM).
Methods: We retrospectively analyzed MRI and pathological data of 152 GBM patients. A total of 2,832 radiomics features were extracted and filtered from preoperative MRI images.