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Relevant bibliographies by topics / Gliomas classification

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Academic literature on the topic 'Gliomas classification'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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Journal articles on the topic "Gliomas classification"

1

Faraji-Rad, Mohammad. "Epidemiological Study of Molecular and Genetic Classification in Adult Diffuse Glioma." International Journal of Surgery & Surgical Techniques 6, no. 2 (2022): 1–5. http://dx.doi.org/10.23880/ijsst-16000171.

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Background: Mutations in isocitrate dehydrogenase 1 (IDH1) and isocitrate dehydrogenase 2 (IDH2) are frequent in lowgrade and high-grade gliomas. However, the diagnostic criteria, in particular for gliomas, are highly various. The aim of our study was to establish genetic profiles for mutation and calcification of diffuse gliomas and to evaluate their predictive factors. Methods: We estimate the different clinical and molecular characterization between IDH1, IDH2 mutant gliomas, p53, ATRX and 1p19q. In addition, whole-transcriptome sequencing and DNA extraction data were used to evaluate the d

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Kwikima, Ugumba. "GLIOMA-04 BRIDGING THE GAP ON ADULT GLIOMA IMAGING, DIAGNOSIS AND FOLLOW UP IN SUB-SAHARAN AFRICA." Neuro-Oncology Advances 5, Supplement_4 (2023): iv1. http://dx.doi.org/10.1093/noajnl/vdad121.003.

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Abstract Cerebral Gliomas are the most common and devastating primary brain tumors. Prior to 2016 WHO CNS tumor classification update, the grading of gliomas, mainly relied on histological features, including cellularity, nuclear atypia, mitotic activity, vascularity, and necrosis, observed on light microscopy with the aid of immunohistochemistry. A number of studies confirmed that diffuse gliomas demonstrates different growth pattern, clinical behavior, and prognostication based on their genomic alterations, these findings necessitated incorporation of molecular subtypes in glioma classificat

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Kalidindi, Navya, Rosemarylin Or, Sam Babak, and Warren Mason. "Molecular Classification of Diffuse Gliomas." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 47, no. 4 (2020): 464–73. http://dx.doi.org/10.1017/cjn.2020.10.

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ABSTRACT:Technological advances in the field of molecular genetics have improved the ability to classify brain tumors into subgroups with distinct clinical features and important therapeutic implications. The World Health Organization’s newest update on classification of gliomas (2016) incorporated isocitrate dehydrogenase 1 and 2 mutations, ATRX loss, 1p/19q codeletion status, and TP53 mutations to allow for improved classification of glioblastomas, low-grade and anaplastic gliomas. This paper reviews current advances in the understanding of diffuse glioma classification and the impact of mol

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Hauser, Peter. "Classification and Treatment of Pediatric Gliomas in the Molecular Era." Children 8, no. 9 (2021): 739. http://dx.doi.org/10.3390/children8090739.

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The overall survival of pediatric gliomas varies over a wide spectrum depending on the tumor grade. Low-grade gliomas have an excellent long-term survival, with a possible burden of surgery, irradiation, and chemotherapy; in contrast, high-grade gliomas generally have a short-term, devastating lethal outcome. Recent advances in understanding their molecular background will transform the classification and therapeutic approaches of pediatric gliomas. Molecularly targeted treatments may acquire a leading role in the primary treatment of low-grade gliomas and may provide alternative therapeutic s

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Hervey-Jumper, Shawn L., Jing Li, Joseph A. Osorio, et al. "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, no. 2 (2016): 482–88. http://dx.doi.org/10.3171/2015.4.jns1521.

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OBJECT Though challenging, maximal safe resection of insular gliomas enhances overall and progression-free survival and deters malignant transformation. Previously published reports have shown that surgery can be performed with low morbidity. The authors previously described a Berger-Sanai zone classification system for insular gliomas. Using a subsequent dataset, they undertook this study to validate this zone classification system for predictability of extent of resection (EOR) in patients with insular gliomas. METHODS The study population included adults who had undergone resection of WHO G

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Billard, P., C. Guerriau, C. Carpentier, et al. "OS02.6.A The TeloDIAG: How telomeric parameters can help in glioma rapid diagnosis and liquid biopsies approaches." Neuro-Oncology 23, Supplement_2 (2021): ii5—ii6. http://dx.doi.org/10.1093/neuonc/noab180.015.

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Abstract BACKGROUND The integration of molecular markers into the WHO 2016 classification has clarified the complex diagnosis of gliomas. Among these biomarkers, the TERT promoter mutation and the loss of ATRX (ATRX loss) are mutually exclusive alterations associated with re-activation of telomerase or alternative lengthening of telomeres (ALT), respectively. Strangely, 25% of gliomas display neither or both these alterations, a situation referred to as abnormal telomere maintenance mechanism (aTMM). MATERIAL AND METHODS To investigate the TMM actually involved in gliomas, the C-circle (CC) as

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Cinarer, Gokalp, and Bulent Gursel Emiroglu. "Classification of brain tumours using radiomic features on MRI." New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, no. 12 (April 30, 2020): 80–90. http://dx.doi.org/10.18844/gjpaas.v0i12.4989.

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Glioma is one of the most common brain tumours among the diagnoses of existing brain tumours. Glioma grades are important factors that should be known in the treatment of brain tumours. In this study, the radiomic features of gliomas were analysed and glioma grades were classified by Gaussian Naive Bayes algorithm. Glioma tumours of 121 patients of Grade II and Grade III were examined. The glioma tumours were segmented with the Grow Cut Algorithm and the 3D feature of tumour magnetic resonance imaging images were obtained with the 3D Slicer programme. The obtained quantitative values were stat

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Im, Sanghyuk, Jonghwan Hyeon, Eunyoung Rha, et al. "Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning." Sensors 21, no. 10 (2021): 3500. http://dx.doi.org/10.3390/s21103500.

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Diffuse gliomas are the most common primary brain tumors and they vary considerably in their morphology, location, genetic alterations, and response to therapy. In 2016, the World Health Organization (WHO) provided new guidelines for making an integrated diagnosis that incorporates both morphologic and molecular features to diffuse gliomas. In this study, we demonstrate how deep learning approaches can be used for an automatic classification of glioma subtypes and grading using whole-slide images that were obtained from routine clinical practice. A deep transfer learning method using the ResNe

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Chen, Jiaqi, Jin Yang, Jinyu Wang, et al. "Study on an Automatic Classification Method for Determining the Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-Scale Spatial–Spectral Fusion Features." Sensors 24, no. 12 (2024): 3803. http://dx.doi.org/10.3390/s24123803.

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This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLMER-ResNet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention m

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Yuan, Feng, Yingshuai Wang, Lei Yuan, et al. "Machine learning–based new classification for immune infiltration of gliomas." PLOS ONE 19, no. 10 (2024): e0312071. http://dx.doi.org/10.1371/journal.pone.0312071.

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Background Glioma is a highly heterogeneous and poorly immunogenic malignant tumor, with limited efficacy of immunotherapy. The characteristics of the immunosuppressive tumor microenvironment (TME) are one of the important factors hindering the effectiveness of immunotherapy. Therefore, this study aims to reveal the immune microenvironment (IME) characteristics of glioma and predict different immune subtypes using machine learning methods, providing guidance for immune therapy in glioma. Methods We first performed unsupervised cluster analysis on the genes and arrays of 693 gliomas in CGGA dat

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Dissertations / Theses on the topic "Gliomas classification"

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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.

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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.

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Les gliomes sont des tumeurs agressives de mauvais pronostic, très angiogéniques et infiltrantes ce qui rend leur exérèse particulièrement difficile. Vu les limites des techniques actuelles d’imagerie, nous avons proposé la spectro-imagerie Infrarouge à Transformée de Fourier (IRTF), d’une résolution spatiale de 6 µm, pour apporter une information moléculaire à l’examen histologique actuel des gliomes. Nos travaux ont été fondés sur la recherche de paramètres moléculaires des vaisseaux sanguins, notamment sur la base des contenus de leur membrane basale. Celle-ci subit des altérations dûes au

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Ritt, Philipp [Verfasser], and 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.

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Steinmeier, Ralf, Stephan B. Sobottka, Gilfe Reiss, Jan Bredow, Johannes Gerber, and 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.

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The identification of eloquent areas is of utmost importance in the surgery of tumors located near speech-eloquent brain areas, since the classical concept of a constant localization was proven to be untrue and the spatial localization of these areas may show large interindividual differences. Some neurosurgical centers apply intraoperative electrophysiological methods that, however, necessitate the performance of surgery in the awake patient. This might be a severe burden both for the patient and the operating team in a procedure that lasts several hours; in addition, electrical stimulation m

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Li, Yingping. "Artificial intelligence and radiomics in cancer diagnosis." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG053.

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L'intelligence artificielle (IA) est très utilisée pour le diagnostic et le traitement de données médicales, donnant lieu à la médecine personnalisée assistée par l'IA. Ce manuscrit se concentre sur la proposition et l'analyse de méthodes d'IA, notamment l'apprentissage profond et la radiomique, pour le diagnostic du cancer. Tout d'abord, une approche efficace de segmentation automatique est essentielle pour mettre en place une méthode de diagnostic par IA, car c'est un préalable à une analyse par radiomiques. Nous avons proposé une nouvelle approche pour la segmentation automatique des lésion

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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.

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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.

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Les gliomes, tumeurs cérébrales primaires du système nerveux central, sont souvent de pronostic défavorable, d'autant plus que l'absence de critères indiscutables pour les identifier rend leur diagnostic et leur prise en charge particulièrement difficiles. L’analyse conjointe, d’une cohorte française de 64 patients porteurs de gliomes et d’une cohorte internationale de 671 patients issus du TCGA, a permis de mettre en évidence deux groupes pronostiques constitués par un panel d’expression différentielle de 26 gènes (p = 0,007). Cette stratification en deux groupes pronostiques a été confirmée

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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.

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L’incidence des gliomes est estimée à 6.6 pour 100 000 habitants. Les survies varient selon le sous-type de gliomes, avec des taux de survie à 5 ans d’environ 48% pour les astrocytomes diffus selon la classification de l’Organisation Mondiale de la Santé (OMS), 28% pour les astrocytomes anaplasiques, 80% pour les oligodendrogliomes, 52% pour les oligodendrogliomes anaplasiques et 5% pour les glioblastomes, tumeurs cérébrales malignes les plus fréquentes.Une meilleure compréhension des mécanismes et de la biologie de ces tumeurs et de nouvelles pistes thérapeutiques sont essentielles afin d’ide

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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.

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Anaplastic or Grade III glioma form a diverse group of tumours with a range of prognoses that have been historically managed with surgery followed by surveillance or adjuvant radiation therapy. The onset of molecular testing, specifically the recognition of the IDH1/2 mutations, has been able to identify a subgroup of patients with a more favourable prognosis. With this knowledge there is the need to optimise management, not only consolidate the improved prognosis of these patients, but also to minimise the late morbidity of treatment protocols. The initial chapters of this thesis outli

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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.

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La métabolomique est un outil récent, aujourd'hui en plein essor, qui se définit comme étant l'étude de l'ensemble des petites molécules ; les métabolites, produites par un organisme sous différentes conditions. Son application à la problématique du cancer consiste en l'étude des perturbations métaboliques liées au processus oncologique. La métabolomique mêle diverses techniques d'analyses telles que la spectroscopie de masse et la spectroscopie de résonance magnétique nucléaire (RMN), couplées à des méthodes statistiques évoluées afin d'extraire les informations métaboliques pertinentes perme

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Books on the topic "Gliomas classification"

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Adamson, David Cory. Gliomas: Classification, Symptoms, Treatment and Prognosis. Nova Science Publishers, Incorporated, 2014.

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Pandey, Sanjeet, Dr Sheshang Degadwala, and Dr Vineet Kumar Singh. BRAIN TUMOR CLASSIFICATION INTO HIGH AND LOW GRADE GLIOMAS. Scholars' Press, 2021.

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Kleihues, Paul, Elisabeth Rushing, and 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.

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The revised fourth edition of the WHO classification of Tumours of the Central Nervous System, published in 2016, comprises several newly recognized tumour entities, and a significant restructuring of the classification, mainly based on genetic profiling. Glioblastomas are now classified into two major types. Isocitrate dehydrogenase (IDH)-wildtype glioblastoma (primary glioblastoma IDH-wildtype) develops rapidly de novo without a recognizable precursor lesion. IDH-mutant glioblastoma (secondary glioblastoma IDH-mutant) develops more slowly through malignant progression from diffuse or anaplas

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Book chapters on the topic "Gliomas classification"

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Wesseling, Pieter. "Classification of Gliomas." In Emerging Concepts in Neuro-Oncology. Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-458-6_1.

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Kato, Kikuya. "Molecular Classification of Gliomas." In Tumors of the Central Nervous System, Volume 1. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0344-5_2.

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Rigau, Valérie. "Histological Classification." In Diffuse Low-Grade Gliomas in Adults. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-2213-5_3.

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Allinson, Kieren S. J. "The Classification of Adult Gliomas." In Management of Adult Glioma in Nursing Practice. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-76747-5_7.

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Shi, Zhifeng. "Advances in Molecular Classification of Gliomas." In Experts' Perspectives on Medical Advances. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-0123-4_2.

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Rigau, Valérie. "Towards an Intermediate Grade in Glioma Classification." In Diffuse Low-Grade Gliomas in Adults. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55466-2_5.

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Purkait, Suvendu. "Pathology, Molecular Biology and Classification of Gliomas." In Evidence based practice in Neuro-oncology. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2659-3_3.

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Velázquez Vega, José E., and Daniel J. Brat. "Molecular-Genetic Classification of Gliomas and Its Practical Application to Diagnostic Neuropathology." In Diffuse Low-Grade Gliomas in Adults. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55466-2_4.

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Ohgaki, Hiroko. "Contribution of Molecular Biology to the Classification of Low-Grade Diffuse Glioma." In Diffuse Low-Grade Gliomas in Adults. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-2213-5_5.

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Quinones, Addison, and Anne Le. "The Multifaceted Glioblastoma: From Genomic Alterations to Metabolic Adaptations." In The Heterogeneity of Cancer Metabolism. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65768-0_4.

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AbstractGlioblastoma multiforme (GBM) develops on glial cells and is the most common as well as the deadliest form of brain cancer. As in other cancers, distinct combinations of genetic alterations in GBM subtypes induce a diversity of metabolic phenotypes, which explains the variability of GBM sensitivity to current therapies targeting its reprogrammed metabolism. Therefore, it is becoming imperative for cancer researchers to account for the temporal and spatial heterogeneity within this cancer type before making generalized conclusions about a particular treatment’s efficacy. Standard therap

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Conference papers on the topic "Gliomas classification"

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Tas, Muhammed Oguz, Hasan Serhan Yavuz, and Muhammed Alperen Tas. "MRI Based High-Grade and Low-Grade Brain Gliomas Classification by Using Video Vision Transformers." In 2024 Medical Technologies Congress (TIPTEKNO). IEEE, 2024. http://dx.doi.org/10.1109/tiptekno63488.2024.10755474.

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Elayavalli, Satvik R., Ipsa Yadav, Grant T. Cookson, et al. "Non-Invasive Molecular Classification of Gliomas Using Explainable ai and Radiomic Features from Multi-Sequence MRI." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981077.

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Janardhan, Kiranmayee, and Christy Bobby T. "MRI Fusion for Glioma Grade Classification." In 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2024. http://dx.doi.org/10.1109/conecct62155.2024.10677196.

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Ibrahim, Muhammad Daffa’, Siti Sa’Adah, and Isman Kurniawan. "Optimizing Multilayer Perceptron Using Genetic Algorithm for Glioma Classification." In 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). IEEE, 2024. https://doi.org/10.1109/comnetsat63286.2024.10862533.

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Chen, Qijian, Lihui Wang, Zeyu Deng, Li Wang, Chen Ye, and YueMin Zhu. "Tumor State-Space Network for High and Low Grade Glioma Classification." In 2024 IEEE 17th International Conference on Signal Processing (ICSP). IEEE, 2024. https://doi.org/10.1109/icsp62129.2024.10846313.

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Yamagishi, Yosuke, and Shouhei Hanaoka. "Cell Image Classification with Polygon-Guided Extraction for Glioma Mitosis Detection." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980953.

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Chen, Qiao, Hongming Xu, Xinyu Hao, et al. "Distilling Genomic Knowledge into Whole Slide Imaging for Glioma Molecular Classification." In 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2025. https://doi.org/10.1109/cbms65348.2025.00112.

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Guo, Shunchao, Yanxiang Yuan, Yang Chen, and Tuntun Wang. "Ensemble learning for glioma classification based on radiomics: a multimodal study." In International Symposium on Artificial Intelligence Innovations (ISAII 2025), edited by Xin Xu and Weihua Ou. SPIE, 2025. https://doi.org/10.1117/12.3074278.

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A S, Vickram, and Mathan Muthu C M. "Automatic Classification of Brain MRI slices into Glioma/Meningioma using Deep Transfer-Learning." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915481.

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Rasham, Razaul, ASM Shihavuddin, D. M. Saaduzzaman, Anwar Hossain, Shimul Das, and Molla Shahadat Hossain Lipu. "GlioSeg 3D: Advancing Glioma Brain Tumor Classification & Segmentation with 3D MRI Data." In 2024 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON). IEEE, 2024. https://doi.org/10.1109/peeiacon63629.2024.10800098.

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