Publications by authors named "Yuying Zang"
Article Synopsis
- The study investigates how clinicopathological and radiological factors influence false-positive and false-negative results for isocitrate dehydrogenase (IDH) mutations in gliomas, focusing on the T2-FLAIR mismatch sign in MRI.
- Out of 1515 patients with gliomas, the false-positive rate was 3.5% and was influenced by factors like patient age and non-restricted diffusion.
- Conversely, the false-negative rate was significantly higher at 39.7% and correlated with patient age, 1p/19q co-deletion, and telomerase promoter mutation, indicating that while misinterpretations can occur, careful evaluation can minimize errors.
Objectives: To preoperatively predict the high expression of Ki67 and positive pituitary transcription factor 1 (PIT-1) simultaneously in pituitary adenoma (PA) using three different radiomics models.
Methods: A total of 247 patients with PA (training set: n = 198; test set: n = 49) were included in this retrospective study. The imaging features were extracted from preoperative contrast-enhanced T1WI (T1CE), T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI).
Using radiomics to predict O6-methylguanine-DNA methyltransferase promoter methylation status in patients with newly diagnosed glioblastoma and compare the performances of different MRI sequences. Preoperative MRI scans from 215 patients were included in this retrospective study. After image preprocessing and feature extraction, two kinds of machine-learning models were established and compared for their performances.
View Article and Find Full Text PDFRadiogenomic Analysis of Vascular Endothelial Growth Factor in Patients With Glioblastoma.
J Comput Assist Tomogr
November 2023
Objectives: This article aims to predict the presence of vascular endothelial growth factor (VEGF) expression and to predict the expression level of VEGF by machine learning based on preoperative magnetic resonance imaging (MRI) of glioblastoma (GBM).
Methods: We analyzed the axial T2-weighted images (T2WI) and T1-weighted contrast-enhancement images of preoperative MRI in 217 patients with pathologically diagnosed GBM. Patients were divided into negative and positive VEGF groups, with the latter group further subdivided into low and high expression.
Background: Differentiation between cerebral glioblastoma multiforme (GBM) and solitary brain metastasis (MET) is important. The existing radiomic differentiation method ignores the clinical and routine magnetic resonance imaging (MRI) features.
Purpose: To differentiate between GBM and MET and between METs from the lungs (MET-lung) and other sites (MET-other) through clinical and routine MRI, and radiomics analyses.
Objective: To identify optimal machine-learning methods for the radiomics-based differentiation of gliosarcoma (GSM) from glioblastoma (GBM).
Materials And Methods: This retrospective study analyzed cerebral magnetic resonance imaging (MRI) data of 83 patients with pathologically diagnosed GSM (58 men, 25 women; mean age, 50.5 ± 12.