Intracranial neoplasia is frequently encountered in dogs. After a presumptive diagnosis of intracranial neoplasia is established based on history, clinical signs and advanced imaging characteristics, the decision to treat and which treatment to choose must be considered. The objective of this study is to report survival times (ST) for dogs with intracranial meningiomas and gliomas treated with surgical resection alone (SRA), to identify potential prognostic factors affecting survival, and to compare the results with the available literature. Medical records of 29 dogs with histopathologic confirmation of intracranial meningiomas and gliomas treated with SRA were retrospectively reviewed. For each dog, signalment, clinical signs, imaging findings, type of surgery, treatment, histological evaluation, and ST were obtained. Twenty-nine dogs with a histological diagnosis who survived >7 days after surgery were included. There were 15 (52%) meningiomas and 14 (48%) gliomas. All tumors had a rostrotentorial location. At the time of the statistical analysis, only two dogs were alive. Median ST for meningiomas was 422 days (mean, 731 days; range, 10-2735 days). Median ST for gliomas was 66 days (mean, 117 days; range, 10-730 days). Kaplan-Meier analysis indicated that ST was significantly longer for meningiomas than for gliomas (<0.05). A negative correlation between the presence of a midline shift and ST (=0.037) and ventricular compression and ST (=0.038) was observed for meningiomas. For gliomas, there were no significant associations between ST and any of the variables evaluated. In conclusion, the results of this study suggest that, for dogs that survived >7 days postoperatively, SRA might be an appropriate treatment, particularly for meningiomas, when radiation therapy is not readily available. Also, the presence of midline shift and ventricular compression might be negative prognostic factors for dogs with meningiomas.
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http://dx.doi.org/10.4314/ovj.v7i4.14 | DOI Listing |
Cancers (Basel)
January 2025
Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
Background/objectives: Brain tumor classification is a crucial task in medical diagnostics, as early and accurate detection can significantly improve patient outcomes. This study investigates the effectiveness of pre-trained deep learning models in classifying brain MRI images into four categories: Glioma, Meningioma, Pituitary, and No Tumor, aiming to enhance the diagnostic process through automation.
Methods: A publicly available Brain Tumor MRI dataset containing 7023 images was used in this research.
Sci Rep
January 2025
Institute of Optoelectronics, Military University of Technology, Gen. S. Kaliskiego 2, Warsaw, 00-908, Poland.
Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separable convolutional neural network (PDSCNN) and a hybrid ridge regression extreme learning machine (RRELM) for accurately classifying four types of brain tumors (glioma, meningioma, no tumor, and pituitary) based on MRI images. The proposed approach enhances the visibility and clarity of tumor features in MRI images by employing contrast-limited adaptive histogram equalization (CLAHE).
View Article and Find Full Text PDFPLoS One
January 2025
Department of Neurosurgery, Osaka University Graduate School of Medicine, Osaka, Japan.
Background: Glioblastoma is characterized by neovascularization and diffuse infiltration into the adjacent tissue. T2*-based dynamic susceptibility contrast (DSC) MR perfusion images provide useful measurements of the biomarkers associated with tumor perfusion. This study aimed to distinguish infiltrating tumors from vasogenic edema in glioblastomas using DSC-MR perfusion images.
View Article and Find Full Text PDFMedicine (Baltimore)
January 2025
Department of Pathology, Deyang Peoples' Hospital, Deyang, Sichuan Province, China.
Rationale: Ependymomas are commonly prevalent intramedullary neoplasms in adults, with hardly any cases of exophytic extramedullary ependymoma being reported. Meningiomas, on the contrary, are one of the most common intradural extramedullary (IDEM) tumors. However, the occurrence of both IDEM tumors simultaneously is extremely rare.
View Article and Find Full Text PDFJ Natl Cancer Inst
January 2025
Division of Pediatric Hematology & Oncology, University of Minnesota, Minneapolis, MN, USA.
Purpose: It is not known whether temporal changes in childhood cancer therapy have reduced risk of subsequent malignant neoplasms (SMNs) of the central nervous system (CNS), a frequently fatal late effect of cancer therapy.
Methods: Five-year survivors of primary childhood cancers diagnosed between 1970-1999 in the Childhood Cancer Survivor Study with a subsequent CNS SMN were identified. Cumulative incidence rates and standardized incidence ratios (SIR) were compared among survivors diagnosed between 1970-1979 (N = 6223), 1980-1989 (N = 9680), and 1990-1999 (N = 8999).
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