There are few studies reporting the incidence of leptomeningeal dissemination (LMD) in patients with glioblastoma; only small case series have been published. Consequently, there are no established standards of care for these patients. Therefore, we undertook this retrospective review to evaluate a large series of patients with glioblastoma treated at MD Anderson Cancer Center to estimate the incidence of LMD and assess the impact of a variety of treatment modalities. Analysis was performed on 595 patients with glioblastoma treated on clinical trials from 2006 to 2012. The diagnosis of LMD was made by imaging or positive cerebrospinal fluid cytology in 24 patients. An additional 12 patients with known LMD diagnosed during this same period were included to evaluate the impact of treatment on outcome for a total of 36 patients. LMD developed in 4.0 % (24/595 patients) of the clinical trial cohort. Median survival from glioblastoma diagnosis was 16.0 months. Estimated median time of glioblastoma diagnosis to LMD was 11.9 months. Median overall survival from the time of LMD diagnosis was 3.5 months. Patients treated for LMD with chemotherapy/targeted therapy and radiation had a significantly prolonged survival (7.7 months) compared to chemotherapy/targeted therapy alone, radiation alone or palliative care. LMD remains an uncommon event in patients with glioblastoma. Patients treated aggressively with chemotherapy/targeted therapy and radiation had the longest median survival following diagnosis of LMD. However, patients receiving chemotherapy/targeted therapy and radiation were younger and this may have influenced survival. Given the overall poor outcomes, improved therapeutic approaches are needed for glioblastoma patients with LMD.
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http://dx.doi.org/10.1007/s11060-014-1592-1 | 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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