Objective: The aim of this study is to analyze the quantitative DTI parameters of the CST in patients suffering from subcortical gliomas affecting the CST using generally available navigation software.
Methods: A retrospective study was conducted on 22 subjects with diagnosis of primary cerebral glioma and preoperative motor deficits. Exclusion criteria were: involvement of motor cortex, lesion involving both hemispheres, previous surgical treatment. All patients were studied using magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) sequences. Volume, fractional anisotropy (FA) and mean diffusivity value (MD) of the entire CSTs were estimated. Moreover, distance from midline, diameters, FA and MD were calculated on axial images at the point of minimal distance between tumor and CST. Statistical analysis was performed.
Results: There was a statistically significant difference of CST volume between affected and non-affected hemispheres (p<0.01). Mean overall/local FA, overall/local MD and sagittal diameter of CST were also significantly different between the two sides (p<0.05). Correlation tests resulted positive between the shift of CST and overall/local MD. Moreover there is significance between CST volume of tumor hemisphere and preoperative duration of motor deficits (p<0.05).
Conclusion: The present study has demonstrated for the first time a significant difference of DTI based quantitative parameters of the CST between a tumor affected and a non-affected hemisphere in patients with a corresponding motor deficit. This preliminary data suggests a correlation between DTI based integrity of CST and its function.
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http://dx.doi.org/10.1016/j.clineuro.2015.05.004 | DOI Listing |
Curr Oncol
December 2024
Neurosurgery Unit, Head-Neck and NeuroSciences Department University Hospital of Udine, 33100 Udine, Italy.
Background: Tractography allows the in vivo study of subcortical white matter, and it is a potential tool for providing predictive indices on post-operative outcomes. We aim at establishing whether there is a relation between cognitive outcome and the status of the inferior fronto-occipital fasciculus's (IFOF's) microstructure.
Methods: The longitudinal neuropsychological data of thirty young (median age: 35 years) patients operated on for DLGG in the left temporo-insular cortex along with pre-surgery tractography data were processed.
Acta Neurochir (Wien)
December 2024
Department of Neurosurgery, King's College Hospital, Denmark Hill, SE5 9RS, London, UK.
Neuroplasticity is well established in low grade glioma patients. Less is known about functional plasticity in glioblastomas. A 56-year-old lady presented with a recurrent speech deficit seventeen months after her initial craniotomy for a language eloquent glioblastoma (GBM).
View Article and Find Full Text PDFCurr Oncol
October 2024
Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON M5S 1A1, Canada.
Despite significant advancements in neuro-oncology, management of glioblastoma remains a formidable challenge. Over the last century, the role and goals of surgery for patients with glioblastoma have evolved dramatically, with surgical intervention maintaining a central role in patient care. To understand the future role of surgery in the management of glioblastoma, we must review and appreciate the historical journey that has led us to this juncture.
View Article and Find Full Text PDFJ Neurooncol
January 2025
Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
Purpose: Treatment response assessment for gliomas currently uses changes in tumour size as measured with T- and T-weighted MRI. However, changes in tumour size may occur many weeks after therapy completion and are confounded by radiation treatment effects. Advanced MRI techniques sensitive to tumour physiology may provide complementary information to evaluate tumour response at early timepoints during therapy.
View Article and Find Full Text PDFDiagnostics (Basel)
October 2024
AI Center, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea.
Background: The aim of this review is to highlight the new advance of predictive and explainable artificial intelligence for neuroimaging applications.
Methods: Data came from 30 original studies in PubMed with the following search terms: "neuroimaging" (title) together with "machine learning" (title) or "deep learning" (title). The 30 original studies were eligible according to the following criteria: the participants with the dependent variable of brain image or associated disease; the interventions/comparisons of artificial intelligence; the outcomes of accuracy, the area under the curve (AUC), and/or variable importance; the publication year of 2019 or later; and the publication language of English.
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