Prognosis and management of gliosarcoma patients: A review of literature

Saadeh, Fadi; Iskandarani, Sarah El; Najjar, Marwan; Assi, Hazem

doi:10.1016/j.clineuro.2019.05.008

Cited by 13 publications

(30 citation statements)

References 35 publications

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“…GSM is associated with lower ratios of epidermal growth factor receptor (EGFR) and O 6 methylguanine-DNA methyltransferase (MGMT) promoter methylation without isocitrate dehydrogenase (IDH) mutations as well as the expression of the v-raf murine sarcoma viral oncogene homolog B1(BRAF) gene at codon 600 (BRAF V600E) (2)(3)(4)(5)(6). Clinically, GSM is associated with a higher ratio of extracranial metastasis (7,8) and a poorer prognosis (3,(9)(10)(11). These molecular, genetic, and clinical differences between GSM and GBM indicate that the former may be treated as a unique entity.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma

Qian

Zhang

et al. 2021

Front. Oncol.

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ObjectiveTo identify optimal machine-learning methods for the radiomics-based differentiation of gliosarcoma (GSM) from glioblastoma (GBM).Materials and MethodsThis retrospective study analyzed cerebral magnetic resonance imaging (MRI) data of 83 patients with pathologically diagnosed GSM (58 men, 25 women; mean age, 50.5 ± 12.9 years; range, 16-77 years) and 100 patients with GBM (58 men, 42 women; mean age, 53.4 ± 14.1 years; range, 12-77 years) and divided them into a training and validation set randomly. Radiomics features were extracted from the tumor mass and peritumoral edema. Three feature selection and classification methods were evaluated in terms of their performance in distinguishing GSM and GBM: the least absolute shrinkage and selection operator (LASSO), Relief, and Random Forest (RF); and adaboost classifier (Ada), support vector machine (SVM), and RF; respectively. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) of each method were analyzed.ResultsBased on tumor mass features, the selection method LASSO + classifier SVM was found to feature the highest AUC (0.85) and ACC (0.77) in the validation set, followed by Relief + RF (AUC = 0.84, ACC = 0.72) and LASSO + RF (AUC = 0.82, ACC = 0.75). Based on peritumoral edema features, Relief + SVM was found to have the highest AUC (0.78) and ACC (0.73) in the validation set. Regardless of the method, tumor mass features significantly outperformed peritumoral edema features in the differentiation of GSM from GBM (P < 0.05). Furthermore, the sensitivity, specificity, and accuracy of the best radiomics model were superior to those obtained by the neuroradiologists.ConclusionOur radiomics study identified the selection method LASSO combined with the classifier SVM as the optimal method for differentiating GSM from GBM based on tumor mass features.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma

Qian

Zhang

et al. 2021

Front. Oncol.

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“…Though many immunotherapy methods, such as GBM vaccines, oncolytic viral therapies, immune-checkpoint suppressors, and chimeric antigen receptor T cell therapy, have been conducted in clinical trials, none of these have been applied for clinical treatment [7,8]. Similarly, though some prognosis biomarkers, including MLK3 and P4HA1, have been identified in GBM at the genetic level, little of these have been applied for the prediction of the prognosis of patients [9][10][11]. Thus, it is necessary to identify novel biomarkers for the prognosis and therapy of GBM.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis of CD163 as a Prognostic Biomarker and Associated with Immune Infiltration in Glioblastoma Multiforme

Wang

et al. 2021

BioMed Research International

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Background. Glioblastoma multiforme (GBM) is the most common and aggressive primary malignancy in adults with high aggression. The prognosis of GBM patients is poor. There is a critical need for novel biomarkers for the prognosis and therapy of GBM. Methods. Differentially expressed genes (DEGs) in GBM were screened using TCGA cohort. Univariate and multivariate Cox regression analyses were performed on DEGs to identify the optimal prognosis-related genes. qRT-PCR was performed to verify the result. Results. A total of 5216 DEGs, including 2785 upregulated and 2458 downregulated genes, were obtained. Enrichment analysis revealed that these DEGs were mainly involved in the p53 signaling pathway and cell cycle, immune response, and MAPK signaling pathways. Moreover, the top 50 DEGs were associated with drug resistance or drug sensitivity. Prognosis analysis revealed that GBM patients with a high expression of CD163 and CHI3L2 had a poor overall survival, prognosis-free survival, and disease-specific survival. The univariate and multivariate analyses revealed that CD163 and age were independent factors affecting the prognosis of GBM patients. A validation study revealed that CD163 was upregulated in GBM tissues and associated with poor overall survival. Moreover, further analysis revealed that CD163 showed significant correlation with immune cells, immune biomarkers, chemokines, and chemokine receptors. We also identified several CD163-associated kinase, miRNA, and transcription factor targets in GBM, including LCK, miR-483, and ELF1. Conclusions. In conclusion, our study suggested CD163 as a prognostic biomarker and associated it with immune infiltration in GBM.

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“…Peak incidence is seen in the sixth and seventh decades of life (1,2). GSM are clinically indistinguishable from GBM (9).…”

Section: Discussionmentioning

confidence: 99%

“…G liosarcomas are a variant of glioblastoma (along with epithelioid glioblastoma and giant cell glioblastoma) recognized in the current WHO classification of CNS tumors (1). They are highly malignant (WHO grade IV) primary intraaxial neoplasms with both glial and mesenchymal elements.…”

Section: Introductionmentioning

confidence: 99%

“…They are highly malignant (WHO grade IV) primary intraaxial neoplasms with both glial and mesenchymal elements. Peak presentation is around the 6th decade and there is a male predilection (1,2). Histologically, GSM tumors are characterized by a biphasic growth pattern consisting of both glial components and areas of sarcomatous, mesenchymal differentiation often resembling fibrosarcoma (3).…”

Section: Introductionmentioning

confidence: 99%

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the Irradiation of Two Cases of Gliosarcoma: Place and Modalities of Radiotherapy

Nouichi¹,

Hassani

Allouche³

et al. 2020

jmbas

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Introduction: Gliosarcoma is a rare histopathologic variant of glioblastoma traditionally associated with a poor prognosis.We present two cases of Gliosarcoma treated in our department. Discussion: Gliosarcoma (GSM) is a variant of glioblastoma (GBM), the most common primary malignant brain tumor that occurs in adults. GSM is characterized by its biphasic components: the gliomatous and sarcomatous components and categorized into primary and secondary GSM. Intrinsic to the brain parenchyma, GSM is usually managed by gross total resection, and radiotherapy with/without chemotherapy. Conclusion: Despite the notable advances and improvement in overall survival (OS), a consensus on the optimal treatment for GSM patients is unclear.

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Prognosis and management of gliosarcoma patients: A review of literature

Cited by 13 publications

References 35 publications

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma

Comprehensive Analysis of CD163 as a Prognostic Biomarker and Associated with Immune Infiltration in Glioblastoma Multiforme

the Irradiation of Two Cases of Gliosarcoma: Place and Modalities of Radiotherapy

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