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Machine learning-based nomogram for distinguishing between supratentorial extraventricular ependymoma and supratentorial glioblastoma.
Front Oncol
September 2024
Department of Radiology, Liuzhou Worker's Hospital, Liuzhou, Guangxi, China.
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.
Ensemble learning-based pretreatment MRI radiomic model for distinguishing intracranial extraventricular ependymoma from glioblastoma multiforme.
NMR Biomed
December 2024
Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, California, USA.
This study aims to develop an ensemble learning (EL) method based on magnetic resonance (MR) radiomic features to preoperatively differentiate intracranial extraventricular ependymoma (IEE) from glioblastoma (GBM). This retrospective study enrolled patients with histopathologically confirmed IEE and GBM from June 2016 to June 2021. Radiomics features were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) sequence images, and classification models were constructed using EL methods and logistic regression (LR).
View Article and Find Full Text PDFSupra-tentorial Ependymomas with ZFTA Fusion, YAP1 Fusion, and Astroblastomas, MN1-altered: Characteristic Imaging Features.
Clin Neuroradiol
December 2024
Pediatric Radiology Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France.
Purpose: Supratentorial (ST) ependymoma subgroups are defined by two different fusions with different prognoses. Astroblastomas, MN1-altered, have ependymal-like histopathologic features and represent a differential diagnosis in children. We hypothesized that ZFTA-fused ependymoma and YAP1-fused ependymoma on the one hand, and astroblastoma, MN1-altered, on the other hand, show different MRI characteristics.
View Article and Find Full Text PDFNomogram incorporating preoperative MRI-VASARI features for differentiating intracranial extraventricular ependymoma from glioblastoma.
Quant Imaging Med Surg
March 2024
Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA.
Background: Intracranial extraventricular ependymoma (IEE) and glioblastoma (GBM) may have similar imaging findings but different prognosis. This study aimed to develop and validate a nomogram based on magnetic resonance imaging (MRI) Visually AcceSAble Rembrandt Images (VASARI) features for preoperatively differentiating IEE from GBM.
Methods: The clinical data and the MRI-VASARI features of patients with confirmed IEE (n=114) and confirmed GBM (n=258) in a multicenter cohort were retrospectively analyzed.
Adult supratentorial extraventricular anaplastic ependymoma with cerebrospinal fluid dissemination metastases: a case report.
Front Neurol
February 2024
Department of Neurosurgery, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, China.
Background: Ependymomas mostly locate in the infratentorial region and often occur in children. Anaplastic ependymomas account for 45-47% of supratentorial and 15-17% of infratentorial ependymomas, also known as malignant ependymomas. Adult supratentorial extraventricular anaplastic ependymoma (SEAE) is rare in clinical practice, and only a few cases have been reported so far, and there is no clinical study with large sample size.
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