Background: High-grade gliomas, with glioblastomas as the most frequently observed histologic subtype, are the most common primary brain tumours in adults. It is considered that inflammatory responses play a major role in malignancies, including tumour progression. This study aimed to determine the prognostic significance of the neutrophil to lymphocyte ratio (NLR) and the thrombocyte to lymphocyte ratio (PLR) as indicators of systemic inflammatory response (SIR) in glioblastoma patients. Methods: A total of 90 patients treated for glioblastoma were retrospectively evaluated. Absolute counts were used to generate NLR and PLR. A SIR was considered to be present with an NLR ≥5 and/or PLR ≥150. Results: Median follow-up time was 11.3 months (range: 1-70 months). The 1-year and 2-year overall survival rates were 55.2% and 19.5%, respectively. Univariate analysis showed that there was no correlation between overall survival and gender (p=0.184), comorbid disease (p = 0.30), clinical presentation (p = 0.884), or tumour lateralization (p = 0.159). Multivariate analysis showed that overall survival was significantly correlated with SIR based on NLR (HR: 2.41), and ECOG performance status (HR: 1.53). The prognostic factors that affected survival, other than SIR, were Eastern Cooperative Oncology Group (ECOG) performance status (p = 0.003), and tumour localization (p = 0.006). Conclusion: The present findings confirm that NLR based on peripheral blood counts prior to treatment can be used as a prognostic factor in patients with glioblastoma. Since tumour aggression increases and survival decreases as the NLR value rises, choice of treatment modality is facilitated for glioblastoma patients.
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http://dx.doi.org/10.22034/APJCP.2017.18.12.3287 | DOI Listing |
Adv Sci (Weinh)
January 2025
The First Hospital of China Medical University, Liaoning, 110001, China.
Glioblastoma multiforme (GBM) is a highly aggressive and malignant brain tumor originating from glial cells, characterized by high recurrence rates and poor patient prognosis. The heterogeneity and complex biology of GBM, coupled with the protective nature of the blood-brain barrier (BBB), significantly limit the efficacy of traditional therapies. The rapid development of nanoenzyme technology presents a promising therapeutic paradigm for the rational and targeted treatment of GBM.
View Article and Find Full Text PDFCurr Oncol
December 2024
Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institute of Health, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Glioblastoma (GBM) is a primary central nervous system malignancy with a median survival of 15-20 months. The presence of both intra- and intertumoral heterogeneity limits understanding of biological mechanisms leading to tumor resistance, including immune escape. An attractive field of research to examine treatment resistance are immune signatures composed of cluster of differentiation (CD) markers and cytokines.
View Article and Find Full Text PDFCells
January 2025
Department of Neuro-Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.
Glioblastoma (GBM) is a highly aggressive brain tumor characterized by its ability to evade the immune system, hindering the efficacy of current immunotherapies. Recent research has highlighted the important role of immunosuppressive macrophages in the tumor microenvironment (TME) in driving this immune evasion. In this study, we are the first to identify as a key regulator of tumor-associated macrophage (TAM)-mediated immunosuppression in GBM.
View Article and Find Full Text PDFBrain Sci
December 2024
Department of Medical and Surgical Sciences and Advanced Technologies "G.F. Ingrassia", Neurological Surgery, Policlinico "G. Rodolico-San Marco" University Hospital, University of Catania, 95124 Catania, Italy.
: Elastic image fusion (EIF) using an intraoperative CT (iCT) scan may enhance neuronavigation accuracy and compensate for brain shift. : To evaluate the safety and reliability of the EIF algorithm (Virtual iMRI Cranial 4.5, Brainlab AG, Munich Germany, for the identification of residual tumour in glioblastoma surgery.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Pathology, University of Yamanashi, Yamanashi 409-3898, Japan.
The latest World Health Organization (WHO) classification of central nervous system tumors (WHO2021/5th) has incorporated molecular information into the diagnosis of each brain tumor type including diffuse glioma. Therefore, an artificial intelligence (AI) framework for learning histological patterns and predicting important genetic events would be useful for future studies and applications. Using the concept of multiple-instance learning, we developed an AI framework named GLioma Image-level and Slide-level gene Predictor (GLISP) to predict nine genetic abnormalities in hematoxylin and eosin sections: , , mutations, promoter mutations, homozygous deletion (CHD), amplification (amp), 7 gain/10 loss (7+/10-), 1p/19q co-deletion, and promoter methylation.
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