Literatura científica selecionada sobre o tema "Classification des gliomes"
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Artigos de revistas sobre o assunto "Classification des gliomes"
Taillibert, Sophie, Marta Pedretti e Marc Sanson. "Classification actuelle des gliomes". La Presse Médicale 33, n.º 18 (outubro de 2004): 1274–77. http://dx.doi.org/10.1016/s0755-4982(04)98906-3.
Texto completo da fonteFigarella-Branger, D., C. Colin, B. Coulibaly, B. Quilichini, A. Maues De Paula, C. Fernandez e C. Bouvier. "Classification histologique et moléculaire des gliomes". Revue Neurologique 164, n.º 6-7 (junho de 2008): 505–15. http://dx.doi.org/10.1016/j.neurol.2008.03.011.
Texto completo da fonteIdbaih, A., Y. Marie, C. Lucchesi, G. Pierron, E. Manié, V. Raynal, K. Hoang-Xuan et al. "Vers une classification moléculaire pronostique des gliomes". Revue Neurologique 163, n.º 1 (janeiro de 2007): 21–23. http://dx.doi.org/10.1016/s0035-3787(07)90373-2.
Texto completo da fonteTaillibert, Sophie, Marta Pedretti e Marc Sanson. "Génétique des gliomes, vers une classification moléculaire". La Presse Médicale 33, n.º 18 (outubro de 2004): 1268–73. http://dx.doi.org/10.1016/s0755-4982(04)98905-1.
Texto completo da fonteBrouland, Jean Philippe, e Andreas F. Hottinger. "Nouvelle classification OMS 2016 des gliomes : quels changements ?" Revue Médicale Suisse 13, n.º 579 (2017): 1805–9. http://dx.doi.org/10.53738/revmed.2017.13.579.1805.
Texto completo da fonteStella, I., M. Helleringer, A. Joud, P. Chastagner e O. Klein. "Gliomes des voies optiques et hypothalamo-chiasmatiques de l’enfant : proposition d’une classification à usage neurochirurgical". Neurochirurgie 65, n.º 2-3 (abril de 2019): 112. http://dx.doi.org/10.1016/j.neuchi.2019.03.024.
Texto completo da fonteLubrano, V., E. Uro-Coste, P. Bousquet, C. Pierroux, M. B. Delisle e J. Lagarrigue. "Étude histo-moléculaire de 26 gliomes de haut grade : impact sur la classification et le pronostic". Neurochirurgie 52, n.º 5 (novembro de 2006): 478. http://dx.doi.org/10.1016/s0028-3770(06)71277-2.
Texto completo da fonteNioche, C., M. Soret, E. Gontier, I. Buvat e G. Bonardel. "Évaluation de la classification des gliomes en TEP 18F-FDOPA obtenue à partir d’un critère quantitatif pour des acquisitions statiques ou dynamiques". Médecine Nucléaire 37, n.º 5 (maio de 2013): 166–67. http://dx.doi.org/10.1016/j.mednuc.2013.03.122.
Texto completo da fonteFaraji-Rad, Mohammad. "Epidemiological Study of Molecular and Genetic Classification in Adult Diffuse Glioma". International Journal of Surgery & Surgical Techniques 6, n.º 2 (2022): 1–5. http://dx.doi.org/10.23880/ijsst-16000171.
Texto completo da fonteZhang, W., J. Zhao, D. Guo, W. Zhong, J. Shu e Y. Luo. "Rôle de l’IRM de susceptibilité magnétique dans la mise en évidence de produits de dégradation de l’hémoglobine intratumoraux et dans la classification des gliomes". Journal de Radiologie 91, n.º 4 (abril de 2010): 485–90. http://dx.doi.org/10.1016/s0221-0363(10)70063-9.
Texto completo da fonteTeses / dissertações sobre o assunto "Classification des gliomes"
Deluche, 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.
Texto completo da fonteGliomas, 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.
Texto completo da fonteThe 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
Wehbe, 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.
Texto completo da fonteMalignant 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
Li, Yingping. "Artificial intelligence and radiomics in cancer diagnosis". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG053.
Texto completo da fonteArtificial intelligence (AI) has been widely used in the research field of AI-assisted diagnosis, treatment, and personalized medicine. This manuscript focuses on the application of artificial intelligence methods including deep learning and radiomics in cancer diagnosis. First, effective image segmentation is essential for cancer diagnosis and further radiomics-based analysis. We proposed a new approach for automatic lesion segmentation in ultrasound images, based on a multicentric and multipathology dataset displaying different types of cancers. By introducing the group convolution, we proposed a lightweight U-net network without sacrificing the segmentation performance. Second, we processed the clinical Magnetic Resonance Imaging (MRI) images to noninvasively predict the glioma subtype as defined by the tumor grade, isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status. We proposed a radiomics-based approach. The prediction performance improved significantly by tuning different settings in the radiomics pipeline. The characteristics of the radiomic features that best distinguish the glioma subtypes were also analyzed. This work not only provided a radiomics pipeline that works well for predicting the glioma subtype, but it also contributed to the model development and interpretability. Third, we tackled the challenge of reproducibility in radiomics methods. We investigated the impact of different image preprocessing methods and harmonization methods (including intensity normalization and ComBat harmonization) on the radiomic feature reproducibility in MRI radiomics. The conclusion showed that ComBat method is essential to remove the nonbiological variation caused by different image acquisition settings (namely, scanner effects) and improve the feature reproducibility in radiomics studies. Meanwhile, intensity normalization is also recommended because it leads to more comparable MRI images and more robust harmonization results. Finally, we investigated improving the ComBat harmonization method by changing its assumption to a very common case that scanner effects are different for different classes (like tumors and normal tissues). Although the proposed model yielded disappointing results, surely due to the lack of enough proper constraints to help identify the parameters, it still paved the way for the development of new harmonization methods
Erb, 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.
Texto completo da fonteCrespin, 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.
Texto completo da fonteThe 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
Colin, Carole. "Mise en évidence et caractérisation fonctionnelle de précurseurs gliaux dans les gliomes humains et identification de marqueurs moléculaires : vers une meilleure compréhension de l'histogenèse et du processus d'invasion". Aix-Marseille 2, 2006. http://www.theses.fr/2006AIX20661.
Texto completo da fonteGliomas are the most frequently occuring primary neoplasms in the central nervous system. The WHO classification remains the standard to classify these tumors, but it suffered from lack of reproducibility. In order to better characterize gliomas, we have studied them in a histological and a molecular point of vue. First, we have shown that gliomas contain a mixture of glial progenitor cells and their progeny. The cells involved in the glioma formation could belong to a glial lineage similar to that observed during development. On the other hand, glioblastomas and pilocytic astrocytomas show differential gene expression patterns, useful for diagnosis. Glioblastomas express a subset of genes involved in invasion and angiogenesis, two processes which are a hallmark of malignancy in these tumours. This work provides cues to glioma histogenesis and to the molecular cartography of these tumors
Duhamel, Marie. "De la classification moléculaire des gliomes à une nouvelle stratégie thérapeutique de réactivation des macrophages au sein de la tumeur". Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10065/document.
Texto completo da fonteTumors are highly heterogeneous both histologicaly and molecularly. In fact, several non-neoplasic cell types are present in the microenvironment. Glioma classification is based on histological criteria that are prone to inter- and intra-observers subjectivities. Within tumors of same grade, subgroups can be differentiated. The aim of this project is to realize a molecular classification of high grade glioma based on proteomics data allowing the localization of potential biomarkers directly on the tissue. Subregions having different molecular profiles have been highlighted and the molecules comprising them have been identified in a localized way. Results prove that histological annotations do not necessarily correspond to molecular classification. This heterogeneity is also found in the tumor microenvironment where we can find immune cells such as macrophages. Macrophages are changed from their primary function by the tumor to allow it to grow. A therapeutic strategy to counter the tumor growth has been developed in order to switch macrophages phenotype toward an antitumor one. The inhibition of PC1/3 enzyme has proven to be a promising therapy to reactivate macrophages via TLR receptors. Secreted factors by these PC1/3 inhibited macrophages have an effect on cancer cells viability and invasion according to TLR ligand used. The first part will allow us to identify subgroups of glioma which, depending on their molecular profiles, could, in a long-term view, receive personalized treatments based on the inhibition of proproteins convertases combined to TLR ligands
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.
Texto completo da fonteBack, 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.
Texto completo da fonteLivros sobre o assunto "Classification des gliomes"
Adamson, David Cory. Gliomas: Classification, Symptoms, Treatment and Prognosis. Nova Science Publishers, Incorporated, 2014.
Encontre o texto completo da fontePandey, Sanjeet, Dr Sheshang Degadwala e Dr Vineet Kumar Singh. BRAIN TUMOR CLASSIFICATION INTO HIGH AND LOW GRADE GLIOMAS. Scholars' Press, 2021.
Encontre o texto completo da fonteDavid N., M.D. Louis (Editor), Hiroko Ohgaki (Editor), Otmar D. Wiestler (Editor) e Webster K. Cavenee (Editor), eds. Who Classification of Tumours of the Central Nervous System (Who Classfication of Tumours). 4a ed. Not Avail, 2007.
Encontre o texto completo da fonteKleihues, Paul, Elisabeth Rushing e 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.
Texto completo da fonteCapítulos de livros sobre o assunto "Classification des gliomes"
Wesseling, Pieter. "Classification of Gliomas". In Emerging Concepts in Neuro-Oncology, 3–20. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-458-6_1.
Texto completo da fonteQuinones, Addison, e Anne Le. "The Multifaceted Glioblastoma: From Genomic Alterations to Metabolic Adaptations". In The Heterogeneity of Cancer Metabolism, 59–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65768-0_4.
Texto completo da fonteKato, Kikuya. "Molecular Classification of Gliomas". In 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.
Texto completo da fonteRigau, Valérie. "Histological Classification". In Diffuse Low-Grade Gliomas in Adults, 31–44. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-2213-5_3.
Texto completo da fonteAllinson, Kieren S. J. "The Classification of Adult Gliomas". In 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.
Texto completo da fonteRigau, Valérie. "Towards an Intermediate Grade in Glioma Classification". In 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.
Texto completo da fontePurkait, Suvendu. "Pathology, Molecular Biology and Classification of Gliomas". In Evidence based practice in Neuro-oncology, 37–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2659-3_3.
Texto completo da fonteJohnson, Derek R., Caterina Giannini e Timothy J. Kaufmann. "Review of WHO 2016 Changes to Classification of Gliomas; Incorporation of Molecular Markers". In Glioma Imaging, 127–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27359-0_8.
Texto completo da fonteVelázquez Vega, José E., e Daniel J. Brat. "Molecular-Genetic Classification of Gliomas and Its Practical Application to Diagnostic Neuropathology". In 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.
Texto completo da fonteOhgaki, Hiroko. "Contribution of Molecular Biology to the Classification of Low-Grade Diffuse Glioma". In Diffuse Low-Grade Gliomas in Adults, 61–72. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-2213-5_5.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Classification des gliomes"
Ul Ain, Qurat, Iqra Duaa, Komal Haroon, Faisal Amin e Muhammad Zia ur Rehman. "MRI Based Glioma Detection and Classification into Low-grade and High-Grade Gliomas". In 2021 15th International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2021. http://dx.doi.org/10.1109/icosst53930.2021.9683838.
Texto completo da fonteKounelakis, M. G., M. E. Zervakis, G. C. Giakos, C. Narayan, S. Marotta, D. Natarajamani, G. J. Postma, L. M. C. Buydens e X. Kotsiakis. "Targeting brain gliomas energy metabolism for classification purposes". In 2010 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2010. http://dx.doi.org/10.1109/ist.2010.5548526.
Texto completo da fonteFelipe, Caio dos Santos, Thatiane Alves Pianoschi Alva, Ana Trindade Winck e Carla Diniz Lopes Becker. "An Approach in Brain Tumor Classification: The Development of a New Convolutional Neural Network Model". In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.233530.
Texto completo da fontePytlarz, Monika, Kamil Wojnicki, Paulina Pilanc-Kudlek, Bozena Kaminska e Alessandro Crimi. "Automated glioma multiclass tumor classification". In Digital and Computational Pathology, editado por John E. Tomaszewski e Aaron D. Ward. SPIE, 2023. http://dx.doi.org/10.1117/12.2654034.
Texto completo da fonteMaddalena, Lucia, Ilaria Granata, Ichcha Manipur, Mario Manzo e Mario Guarracino. "Glioma Grade Classification via Omics Imaging". In 7th International Conference on Bioimaging. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009167700820092.
Texto completo da fonteMaddalena, Lucia, Ilaria Granata, Ichcha Manipur, Mario Manzo e Mario Guarracino. "Glioma Grade Classification via Omics Imaging". In 7th International Conference on Bioimaging. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009167700002513.
Texto completo da fonteCipriano, Carolina L. S., Giovanni L. F. Da Silva, Jonnison L. Ferreira, Aristófanes C. Silva e Anselmo Cardoso De Paiva. "Classification of brain lesions on magnetic resonance imaging using superpixel, PSO and convolutional neural network". In XV Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/wvc.2019.7640.
Texto completo da fontevan der Voort, Sebastian R., Renske Gahrmann, Martin J. van den Bent, Arnaud J. P. E. Vincent, Wiro J. Niessen, Marion Smits e Stefan Klein. "Radiogenomic classification of the 1p/19q status in presumed low-grade gliomas". In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE, 2017. http://dx.doi.org/10.1109/isbi.2017.7950601.
Texto completo da fonteGrzegorzek, Marcin, Marianna Buckan e Sigrid Horn. "Probabilistic classification of intracranial gliomas in digital microscope images based on EGFR quantity". In SPIE Medical Imaging, editado por Josien P. W. Pluim e Benoit M. Dawant. SPIE, 2009. http://dx.doi.org/10.1117/12.811552.
Texto completo da fonteAbas, Fazly S., Hamza N. Gokozan, Behiye Goksel, Jose J. Otero e Metin N. Gurcan. "Intraoperative neuropathology of glioma recurrence: cell detection and classification". In SPIE Medical Imaging, editado por Metin N. Gurcan e Anant Madabhushi. SPIE, 2016. http://dx.doi.org/10.1117/12.2216448.
Texto completo da fonte