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Artykuły w czasopismach na temat "Gliomas classification"
Faraji-Rad, Mohammad. "Epidemiological Study of Molecular and Genetic Classification in Adult Diffuse Glioma". International Journal of Surgery & Surgical Techniques 6, nr 2 (2022): 1–5. http://dx.doi.org/10.23880/ijsst-16000171.
Pełny tekst źródłaKalidindi, Navya, Rosemarylin Or, Sam Babak i Warren Mason. "Molecular Classification of Diffuse Gliomas". Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 47, nr 4 (10.01.2020): 464–73. http://dx.doi.org/10.1017/cjn.2020.10.
Pełny tekst źródłaKwikima, Ugumba. "GLIOMA-04 BRIDGING THE GAP ON ADULT GLIOMA IMAGING, DIAGNOSIS AND FOLLOW UP IN SUB-SAHARAN AFRICA". Neuro-Oncology Advances 5, Supplement_4 (31.10.2023): iv1. http://dx.doi.org/10.1093/noajnl/vdad121.003.
Pełny tekst źródłaHauser, Peter. "Classification and Treatment of Pediatric Gliomas in the Molecular Era". Children 8, nr 9 (27.08.2021): 739. http://dx.doi.org/10.3390/children8090739.
Pełny tekst źródłaHervey-Jumper, Shawn L., Jing Li, Joseph A. Osorio, Darryl Lau, Annette M. Molinaro, Arnau Benet i Mitchel S. Berger. "Surgical assessment of the insula. Part 2: validation of the Berger-Sanai zone classification system for predicting extent of glioma resection". Journal of Neurosurgery 124, nr 2 (luty 2016): 482–88. http://dx.doi.org/10.3171/2015.4.jns1521.
Pełny tekst źródłaCinarer, Gokalp, i Bulent Gursel Emiroglu. "Classification of brain tumours using radiomic features on MRI". New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, nr 12 (30.04.2020): 80–90. http://dx.doi.org/10.18844/gjpaas.v0i12.4989.
Pełny tekst źródłaBillard, P., C. Guerriau, C. Carpentier, F. Juillard, N. Grandin, P. Lomonte, P. Kantapareddy i in. "OS02.6.A The TeloDIAG: How telomeric parameters can help in glioma rapid diagnosis and liquid biopsies approaches". Neuro-Oncology 23, Supplement_2 (1.09.2021): ii5—ii6. http://dx.doi.org/10.1093/neuonc/noab180.015.
Pełny tekst źródłaIm, Sanghyuk, Jonghwan Hyeon, Eunyoung Rha, Janghyeon Lee, Ho-Jin Choi, Yuchae Jung i Tae-Jung Kim. "Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning". Sensors 21, nr 10 (17.05.2021): 3500. http://dx.doi.org/10.3390/s21103500.
Pełny tekst źródłaLewis, Paul D. "Classification of gliomas". Current Diagnostic Pathology 2, nr 3 (wrzesień 1995): 175–80. http://dx.doi.org/10.1016/s0968-6053(05)80056-0.
Pełny tekst źródłaPisapia, David J. "The Updated World Health Organization Glioma Classification: Cellular and Molecular Origins of Adult Infiltrating Gliomas". Archives of Pathology & Laboratory Medicine 141, nr 12 (1.12.2017): 1633–45. http://dx.doi.org/10.5858/arpa.2016-0493-ra.
Pełny tekst źródłaRozprawy doktorskie na temat "Gliomas classification"
Abdel-Hady, Mohamed Helmy Abdel-Rahman. "Molecular genetic profiling of low grade gliomas : towards a molecular genetic classification /". The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486402957195399.
Pełny tekst źródłaWehbe, Katia. "Usage of FTIR spectro-imaging for the development of a molecular anatomo-pathology of cerebral tumors". Thesis, Bordeaux 1, 2008. http://www.theses.fr/2008BOR13677/document.
Pełny tekst źródłaMalignant gliomas are very aggressive tumors with poor prognosis, highly angiogenic and invasive into the surrounding brain parenchyma, making their resection very difficult. Regarding the limits of current imaging techniques, we have proposed Fourier Transform Infrared (FTIR) spectro-imaging, with a spatial resolution of 6 µm, to provide molecular information for the histological examination of gliomas. Our work was based on the research of molecular parameters of blood vessels, notably on the basis of the contents of their basement membrane, which undergoes changes due to tumor angiogenic stress. We have identified alterations of the secondary structure of proteins (such as collagen) in blood vessels during tumor growth. We have also assessed the changes in fatty acyl chains of membrane phospholipids, which revealed a higher unsaturation level in tumor vessels. Then, on a murine glioma model, we have established an efficient method of blood vessels classification based on their carbohydrates and fats contents, allowing the differentiation between healthy and tumor blood vessels. The combination of these parameters was used to provide a molecular histopathology for the study of human gliomas. Our results have demonstrated the feasibility of differentiating between healthy and tumor vasculature in these human gliomas, which help delimitating areas of corresponding tissue. This technique could become a reliable and fast analytical tool, with duration compatible with the surgery and thus very useful for neurosurgeons
Ritt, Philipp [Verfasser], i Joachim [Akademischer Betreuer] Hornegger. "Automated Classification of Cerebral Gliomas by Means of Quantitative Emission Tomography and Multimodal Imaging / Philipp Ritt. Gutachter: Joachim Hornegger". Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2014. http://d-nb.info/1054331413/34.
Pełny tekst źródłaSteinmeier, Ralf, Stephan B. Sobottka, Gilfe Reiss, Jan Bredow, Johannes Gerber i Gabriele Schackert. "Surgery of Low-Grade Gliomas Near Speech-Eloquent Regions: Brainmapping versus Preoperative Functional Imaging". Karger, 2002. https://tud.qucosa.de/id/qucosa%3A27614.
Pełny tekst źródłaDie Identifikation sprachaktiver Areale ist von höchster Bedeutung bei der Operation von Tumoren in der Nähe des vermuteten Sprachzentrums, da das klassische Konzept einer konstanten Lokalisation des Sprachzentrums sich als unrichtig erwiesen hat und die räumliche Ausdehnung dieser Areale eine hohe interindividuelle Varianz aufweisen kann. Einige neurochirurgische Zentren benutzen deshalb intraoperativ elektrophysiologische Methoden, die jedoch eine Operation am wachen Patienten voraussetzen. Dies kann sowohl für den Patienten als auch das Operations-Team eine schwere Belastung bei diesem mehrstündigen Eingriff darstellen, zusätzlich können epileptische Anfälle durch die elektrische Stimulation generiert werden. Alternativ können Modalitäten des «functional brain imaging» (PET, fMRT, MEG usw.) eingesetzt werden, die die individuelle Lokalisation sprachaktiver Areale gestatten. Die Bildfusion dieser Daten mit einem konventionellen 3D-CT oder MRT erlaubt den exakten Transfer dieser Daten in den OP-Situs mittels Neuronavigation. Während Standards bei elektrophysiologischen Stimulationstechniken existieren, die eine permanente postoperative Verschlechterung der Sprachfunktion weitgehend verhindern, bleibt die Relevanz sprachaktiver Areale bei den neuesten bildgebenden Techniken bezüglich einer Operations-bedingten Verschlechterung der Sprachfunktion bisher noch unklar.
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
Dube, Shishir. "An automated system for quantitative hierarchical image analysis of malignant gliomas developing robust techniques for integrated segmentation/classification and prognosis of glioblastoma multiforme /". Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1876284371&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Pełny tekst źródłaDeluche, Mouricout Elise. "Implication des biomarqueurs NTRK2 et CHI3L1 dans la nouvelle classification histo-moléculaire des gliomes". Thesis, Limoges, 2018. http://www.theses.fr/2018LIMO0063/document.
Pełny tekst źródłaGliomas, primary brain tumours of the central nervous system, are often of poor prognosis.The absence of clear criteria to identify them makes their diagnosis and management particularly difficult. The combined analysis of a cohort of 64 glioma patients and an international cohort of 671 patients from the TCGA revealed two prognostic groups of a differential expression panel of 26 genes (p = 0.007). This stratification into two prognostic groups was confirmed independently of the grade and molecular group of the tumor (p <0.0001). We have established a new diagnostic strategy based on the molecular classification of gliomas by integrating two prognostic biomarkers CHI3L1 and NTRK2. Multivariate analysis confirms that these biomarkers are independent of IDH status and tumor grade.While we have demonstrated by the protein analysis of CHI3L1 concordance with the transcripts, the results are different for TrkB. Therefore, a high expression of TrkB and its p75NTR co-receptor would be associated with tumor aggressiveness regardless of IDH status. Lastly, TrkB and p75NTR are present in exosomes from plasma of healthy controls and glioma patients, but their expression increases with the aggressiveness of tumor
Le, Rhun Émilie. "Recherche de biomarqueurs protéiques dans le but de réaliser une classification moléculaire des gliomes : étude GLIOMIC". Thesis, Lille 2, 2017. http://www.theses.fr/2017LIL2S005/document.
Pełny tekst źródłaThe annual incidence of gliomas is estimated at 6.6 per 100,000. Suvival varies profoundly by type of glioma, with 5-year survival rates of 48% for World Health Organization (WHO) grade II diffuse astrocytoma, 28% for WHO grade III anaplastic astrocytomas, 80% for WHO grade II oligodendroglioma, 52% for WHO grade III anaplastic oligodendroglioma and 5% for WHO grade IV glioblastoma, the most frequent primary malignant brain tumor. A better understanding of the molecular pathogenesis and the biology of these tumors is required to design better therapies which can ultimately improve the prognosis of patients. The WHO 2016 classification of central nervous system tumors has for the first time integrated molecular data with the histopathological data, in order to improve the classification of the different subgroups of central nervous system tumors and to allow to derive more specific therapeutic strategies for each of the different subgroups.In the present work, we aimed at evaluating the value of a proteomic approach using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry coupled with microproteomic analysis in gliomas through the GLIOMIC clinical study (NCT02473484), we aimed at obtaining a molecular classification of glioblastomas by integrating clinical data to the ones obtained by such technologies. The feasibility of this approach was first demonstrated in a cohort of anaplastic gliomas. In this first analysis, we showed that although proteomic analysis confirmed the heterogeneity of brain tumors already observed with the histological analysis, the two approaches may lead to different and complementary information. Three different groups of proteins of interest were identified: one involved in neoplasia, one related to glioma with inflammation, and one involved neurogenesis. Then, analyses of glioblastomas confirmed the three proteomic patterns of interest already observed in the anaplastic gliomas, which represents new information as compared to histopathological analysis alone. These results have to be confirmed in a larger cohort of patients.We conclude that MALDI mass spectrometry coupled with microproteomic analysis may provide new diagnostic information and may aid in the identification of new biomarkers. The integration of these proteomic biomarkers into the clinical data, histopathological data and data from molecular biology may improve the knowledge on gliomas, their classification and development of new targeted therapies
Back, Michael. "Optimising the management of anaplastic glioma in the era of molecular classification". Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22332.
Pełny tekst źródłaErb, Gilles. "Application de la RMN HRMAS en Cancérologie “Modèles métaboliques de classification des tumeurs cérébrales”". Phd thesis, Université Louis Pasteur - Strasbourg I, 2008. http://tel.archives-ouvertes.fr/tel-00441765.
Pełny tekst źródłaCrespin, Sophie. "Implications de Cx43 dans les tumeurs gliales humaines : approches in situ et in vitro". Poitiers, 2008. http://theses.edel.univ-poitiers.fr/theses/2008/Crespin-Sophie/2008-Crespin-Sophie-These.pdf.
Pełny tekst źródłaThe possible involvement of Gap-Junctional Intercellular Communication (GJIC) in carcinogenesis has been hypothesized in the 1960s. Later, the expression of connexins, the molecular basis of GJIC, has been shown to “normalize” the phenotype of various tumor cells. Our study, using the tissue micro array approach, was focused on connexin 43 (Cx43) expression in human gliomas (59 tumor samples). We showed that the expression of Cx43 protein was altered and, in several cases, especially in grade-IV gliomas, Cx43 was lost. Nonetheless, due to tumor heterogeneity, a complex pattern of expression was revealed: Cx43 exhibited aberrant staining, that means a translocation into the cytoplasm possibly in the nucleus. Several works suggested that Cx43 could « normalize » tumor cells by a GJIC-independent mechanism. We investigated the role played by Cx43 and different truncated forms of the protein, unable to restore GJIC, in human glioma cell lines. Our data showed that Cx43 expression did not induce any change on cell proliferation when cell lines were maintained in monolayer cultures. On the contrary, the cells trandusced by Cx43 constructs (full-length or truncated) grew less in soft agar assay. In parallel, it appeared that all the Cx43 constructs increased motility. To conclude, Cx43 seems to play a complex role in human glioma progression. Its expression and localization are altered, but the underlying mechanisms remain unknown. Even if Cx43 seems to be altered in gliomas, a maintained expression of the protein could not be correlated with a good prognosis since their motility is increased by Cx43 expression
Książki na temat "Gliomas classification"
Adamson, David Cory. Gliomas: Classification, Symptoms, Treatment and Prognosis. Nova Science Publishers, Incorporated, 2014.
Znajdź pełny tekst źródłaPandey, Sanjeet, Dr Sheshang Degadwala i Dr Vineet Kumar Singh. BRAIN TUMOR CLASSIFICATION INTO HIGH AND LOW GRADE GLIOMAS. Scholars' Press, 2021.
Znajdź pełny tekst źródłaKleihues, Paul, Elisabeth Rushing i Hiroko Ohgaki. The 2016 revision of the WHO classification of tumours of the central nervous system. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199651870.003.0001.
Pełny tekst źródłaDavid N., M.D. Louis (Editor), Hiroko Ohgaki (Editor), Otmar D. Wiestler (Editor) i Webster K. Cavenee (Editor), red. Who Classification of Tumours of the Central Nervous System (Who Classfication of Tumours). Wyd. 4. Not Avail, 2007.
Znajdź pełny tekst źródłaCzęści książek na temat "Gliomas classification"
Wesseling, Pieter. "Classification of Gliomas". W Emerging Concepts in Neuro-Oncology, 3–20. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-458-6_1.
Pełny tekst źródłaKato, Kikuya. "Molecular Classification of Gliomas". W Tumors of the Central Nervous System, Volume 1, 9–19. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0344-5_2.
Pełny tekst źródłaRigau, Valérie. "Histological Classification". W Diffuse Low-Grade Gliomas in Adults, 31–44. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-2213-5_3.
Pełny tekst źródłaAllinson, Kieren S. J. "The Classification of Adult Gliomas". W Management of Adult Glioma in Nursing Practice, 95–107. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-76747-5_7.
Pełny tekst źródłaRigau, Valérie. "Towards an Intermediate Grade in Glioma Classification". W Diffuse Low-Grade Gliomas in Adults, 101–8. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55466-2_5.
Pełny tekst źródłaPurkait, Suvendu. "Pathology, Molecular Biology and Classification of Gliomas". W Evidence based practice in Neuro-oncology, 37–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2659-3_3.
Pełny tekst źródłaQuinones, Addison, i Anne Le. "The Multifaceted Glioblastoma: From Genomic Alterations to Metabolic Adaptations". W The Heterogeneity of Cancer Metabolism, 59–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65768-0_4.
Pełny tekst źródłaVelázquez Vega, José E., i Daniel J. Brat. "Molecular-Genetic Classification of Gliomas and Its Practical Application to Diagnostic Neuropathology". W Diffuse Low-Grade Gliomas in Adults, 73–100. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55466-2_4.
Pełny tekst źródłaOhgaki, Hiroko. "Contribution of Molecular Biology to the Classification of Low-Grade Diffuse Glioma". W Diffuse Low-Grade Gliomas in Adults, 61–72. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-2213-5_5.
Pełny tekst źródłaJohnson, Derek R., Caterina Giannini i Timothy J. Kaufmann. "Review of WHO 2016 Changes to Classification of Gliomas; Incorporation of Molecular Markers". W Glioma Imaging, 127–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27359-0_8.
Pełny tekst źródłaStreszczenia konferencji na temat "Gliomas classification"
Ul Ain, Qurat, Iqra Duaa, Komal Haroon, Faisal Amin i Muhammad Zia ur Rehman. "MRI Based Glioma Detection and Classification into Low-grade and High-Grade Gliomas". W 2021 15th International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2021. http://dx.doi.org/10.1109/icosst53930.2021.9683838.
Pełny tekst źródłaKounelakis, M. G., M. E. Zervakis, G. C. Giakos, C. Narayan, S. Marotta, D. Natarajamani, G. J. Postma, L. M. C. Buydens i X. Kotsiakis. "Targeting brain gliomas energy metabolism for classification purposes". W 2010 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2010. http://dx.doi.org/10.1109/ist.2010.5548526.
Pełny tekst źródłavan der Voort, Sebastian R., Renske Gahrmann, Martin J. van den Bent, Arnaud J. P. E. Vincent, Wiro J. Niessen, Marion Smits i Stefan Klein. "Radiogenomic classification of the 1p/19q status in presumed low-grade gliomas". W 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE, 2017. http://dx.doi.org/10.1109/isbi.2017.7950601.
Pełny tekst źródłaGrzegorzek, Marcin, Marianna Buckan i Sigrid Horn. "Probabilistic classification of intracranial gliomas in digital microscope images based on EGFR quantity". W SPIE Medical Imaging, redaktorzy Josien P. W. Pluim i Benoit M. Dawant. SPIE, 2009. http://dx.doi.org/10.1117/12.811552.
Pełny tekst źródłaTursynbek, Nurislam, Ghazal Ghahramany, Sheida Nabavi i Amin Zollanvari. "Predictive Meta-analysis of Multiple Microarray Datasets: An Application to Classification of Malignant Gliomas". W 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621503.
Pełny tekst źródłaSteiner, Gerald, R. A. Shaw, Lin-P'ing Choo-Smith, Wolfram Steller, Laryssa Shapoval, Gabriele Schackert, Stephan Sobottka, Reiner Salzer i Henry H. Mantsch. "Detection and grading of human gliomas by FTIR spectroscopy and a genetic classification algorithm". W International Symposium on Biomedical Optics, redaktorzy Anita Mahadevan-Jansen, Henry H. Mantsch i Gerwin J. Puppels. SPIE, 2002. http://dx.doi.org/10.1117/12.460789.
Pełny tekst źródłaCipriano, Carolina L. S., Giovanni L. F. Da Silva, Jonnison L. Ferreira, Aristófanes C. Silva i Anselmo Cardoso De Paiva. "Classification of brain lesions on magnetic resonance imaging using superpixel, PSO and convolutional neural network". W XV Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/wvc.2019.7640.
Pełny tekst źródłaFelipe, Caio dos Santos, Thatiane Alves Pianoschi Alva, Ana Trindade Winck i Carla Diniz Lopes Becker. "An Approach in Brain Tumor Classification: The Development of a New Convolutional Neural Network Model". W Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.233530.
Pełny tekst źródłaKong, Jun, Lee Cooper, Fusheng Wang, Candace Chisolm, Carlos Moreno, Tahsin Kurc, Patrick Widener, Daniel Brat i Joel Saltz. "A comprehensive framework for classification of nuclei in digital microscopy imaging: An application to diffuse gliomas". W 2011 8th IEEE International Symposium on Biomedical Imaging (ISBI 2011). IEEE, 2011. http://dx.doi.org/10.1109/isbi.2011.5872833.
Pełny tekst źródłaChakrabarty, Satrajit, Pamela LaMontagne, Joshua Shimony, Daniel S. Marcus i Aristeidis Sotiras. "Non-invasive classification of IDH mutation status of gliomas from multi-modal MRI using a 3D convolutional neural network". W Computer-Aided Diagnosis, redaktorzy Khan M. Iftekharuddin i Weijie Chen. SPIE, 2023. http://dx.doi.org/10.1117/12.2651391.
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