Magnetic resonance imaging findings of intracranial extraventricular ependymoma: A retrospective multi‐center cohort study of 114 cases

Li, Liyan; Fu, Yan; Zhang, Yinping; Mao, Yipu; Huang, Deyou; Yi, Xiaoping; Wang, Jing; Tan, Zeming; Jiang, Muliang; Chen, Bihong T.

doi:10.1002/cam4.6279

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…The clinical variables such as serum creatinine and eosinophils were included in the analysis of this cohort because prior studies have indicated the relevance of these variables for survival and prognosis in patients with GBM ( 23 , 24 ). We have previously published a study on the same cohort focusing on MRI imaging findings of IEE and GBM ( 25 ). The present study extended our research beyond imaging characteristics and tested a machine learning algorithm for differentiating IEE from GBM.…”

Section: Methodsmentioning

confidence: 99%

Nomogram incorporating preoperative MRI-VASARI features for differentiating intracranial extraventricular ependymoma from glioblastoma

Yao,

Fu,

Zhou

et al. 2024

Quant Imaging Med Surg

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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. Predictive models for differentiating IEE from GBM were built using a multivariate logistic regression method. A nomogram was generated and the performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Results The predictors identified in this study consisted of six VASARI features and four clinical features. Compared with the individual models, the combined model incorporating clinical and VASARI features had the highest area under the curve (AUC) value [training set: 0.99, 95% confidence interval (CI): 0.98–1.00; validation set: 0.97, 95% CI: 0.94–1.00] in comparison to the clinical model. The nomogram was well calibrated with significant clinical benefit according to the calibration curve and decision curve analyses. Conclusions The nomogram combining clinical and MRI-VASARI characteristics was robust for differentiating IEE from GBM preoperatively and may potentially assist in diagnosis and treatment of brain tumors.

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Section: Methodsmentioning

confidence: 99%

Nomogram incorporating preoperative MRI-VASARI features for differentiating intracranial extraventricular ependymoma from glioblastoma

Yao,

Fu,

Zhou

et al. 2024

Quant Imaging Med Surg

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Intracranial ependymoma: A retrospective analysis of clinical features, treatment modalities, and long‐term outcome

Badary,

Kurdi,

Almealawy

et al. 2024

Aging Medicine

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ObjectiveEpendymomas, rare neuroglial tumors originating from ependymal cells, can occur in the CNS and typically affect the brain's ventricles or spinal cord. Prognosis is influenced by tumor grade, location, resection extent, and preoperative Karnofsky Performance Status Scale (KPSS) scores. This study evaluates clinical features, treatment outcomes, and factors affecting prognosis in patients with intracranial ependymomas.MethodsA retrospective review of 23 patients with intracranial ependymomas, treated from 2018 to 2023, was conducted. Data included demographics, clinical presentations, KPSS scores, imaging findings, and treatment details. Outcomes assessed were postoperative complications, recurrence rates, and functional status. Statistical analysis used SPSS version 26, with significance set at p < 0.05.ResultsThe cohort was predominantly male (87.0%), with a mean age of 27 years. Tumors were mostly in the fourth ventricle (82.6%), with an average diameter of 68.9 mm. Complete resection was achieved in 87.0% of cases. Postoperative radiotherapy was given to 91.0% of grade 2 and all grade 3 tumors. Recurrence occurred in 17.4% of grade 2 ependymomas, but none of grade 3. The seven‐month mortality rate was 4.3%. Higher preoperative KPSS scores correlated with better outcomes.ConclusionComplete tumor resection and postoperative radiotherapy are crucial for improved outcomes in ependymomas. Higher preoperative KPSS scores and tumor location significantly impact prognosis. Tumors in the lateral ventricles are associated with higher recurrence risks. These findings highlight the need for aggressive surgical management and personalized adjuvant therapy to enhance patient outcomes.

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Ensemble learning‐based pretreatment MRI radiomic model for distinguishing intracranial extraventricular ependymoma from glioblastoma multiforme

He,

Long,

et al. 2024

NMR in Biomedicine

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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). The efficiency of the models was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. The combined EL model, based on clinical parameters and radiomic features from T1WI and T2WI images, demonstrated good discriminative ability, achieving an area under the receiver operating characteristics curve (AUC) of 0.96 (95% CI 0.94–0.98), a specificity of 0.84, an accuracy of 0.92, and a sensitivity of 0.95 in the training set, and an AUC of 0.89 (95% CI 0.83–0.94), a specificity of 0.83, an accuracy of 0.81, and a sensitivity of 0.74 in the validation set. The discriminative efficacy of the EL model was significantly higher than that of the LR model. Favorable calibration performance and clinical applicability for the EL model were observed. The EL model combining preoperative MR‐based tumor radiomics and clinical data showed high accuracy and sensitivity in differentiating IEE from GBM preoperatively, which may potentially assist in clinical management of these brain tumors.

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Magnetic resonance imaging findings of intracranial extraventricular ependymoma: A retrospective multi‐center cohort study of 114 cases

Cited by 5 publications

References 40 publications

Nomogram incorporating preoperative MRI-VASARI features for differentiating intracranial extraventricular ependymoma from glioblastoma

Nomogram incorporating preoperative MRI-VASARI features for differentiating intracranial extraventricular ependymoma from glioblastoma

Intracranial ependymoma: A retrospective analysis of clinical features, treatment modalities, and long‐term outcome

Ensemble learning‐based pretreatment MRI radiomic model for distinguishing intracranial extraventricular ependymoma from glioblastoma multiforme

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