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Academic literature on the topic 'Gliomas classification'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Gliomas classification"

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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 distribution of genetic changes in IDH1 and IDH2 mutant gliomas in a Iranian high grade glioma. Results: Between 2016-2019, among 53 gliomas in our study, 29 cases (54.7% %) harbored an IDH1,2 mutation, 21 cases (39.6 %) harbored an p53 mutation and 19 cases (35.8 %) harbored an ATRX. In addition, 1p19q co-deletion mutation was found in 7 cases (12.2%). We found that IDH1 and IDH2 are mutually entirely in gliomas. There was no significant relation between histopathology, tumor location and clinical finding with diagnosed mutations. Conclusion: Our study discloses an associated distinction between IDH1 and IDH2 mutant gliomas nearly in half of patients, followed by p53. These mutations should be reviewed separately because their differences could have indication for the diagnosis and treatment of IDH1/2 mutant gliomas.

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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 (January 10, 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 molecular markers and DNA methylation studies on survival of patients with these tumors. We also discuss whether the classification and grading of diffuse gliomas should be based on histological findings, molecular markers, or DNA methylation subgroups in future iterations of the classification system.

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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 (October 31, 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 classification, and leads to 2016 WHO CNS tumors Classification update. Introduction of molecular criteria into the classification of gliomas has given rise to interesting, wide-ranging implications regarding glioma management. In the current classification, all diffuse gliomas have been grouped based on their growth pattern, clinical behavior, and specifically sharing of the mutational state of the gene that codes for isocitrate dehydrogenase (IDH) in its isoforms (IDH1 and IDH2). Regardless of grade, the first phase in glioma molecular characterization is IDH testing. Mutations in IDH1 and IDH2 are associated with significant increase in progression free survival and overall survival. Immunohistochemistry, Molecular subtyping, Both FISH and genetic sequencing have significant implications in gliomas management and clinical outcome, yet they are not available in our low resource settings. Advanced MRI techniques (DTI, MR Perfussion, MR Spectroscopy, and Functional MRI) have a significant impact in presurgical planning and follow up of glioma patients after surgery and chemo radiation treatment. Radiographic findings can bridge the gap on accurate glioma diagnosis in sub-Saharan Africa through predicting Glioma Molecular subtypes based on clinical presentation and utilizing conventional MRI as a radio genomic tool. Also to emphasis in the utilization of available advanced MRI techniques (DTI, MR Perfussion, and MR spectroscopy) for presurgical planning and follow up of patients after Glioma treatment.

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Hauser, Peter. "Classification and Treatment of Pediatric Gliomas in the Molecular Era." Children 8, no. 9 (August 27, 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 strategies for high-grade glioma cases in the attempt to avoid the highly unsuccessful conventional therapeutic approaches. This review aims to overview this progress.

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Hervey-Jumper, Shawn L., Jing Li, Joseph A. Osorio, Darryl Lau, Annette M. Molinaro, Arnau Benet, and 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, no. 2 (February 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 Grade II, III, or IV insular gliomas. In accordance with our prior published report, tumor location was classified according to the Berger-Sanai quadrant-style classification system into Zones I through IV. Interobserver variability was analyzed using a cohort of newly diagnosed insular gliomas and independent classification scores given by 3 neurosurgeons at various career stages. Glioma volumes were analyzed using FLAIR and T1-weighted contrast-enhanced MR images. RESULTS One hundred twenty-nine procedures involving 114 consecutive patients were identified. The study population from the authors’ previously published experience included 115 procedures involving 104 patients. Thus, the total experience included 244 procedures involving 218 patients with insular gliomas treated at the authors’ institution. The most common presenting symptoms were seizure (68.2%) and asymptomatic recurrence (17.8%). WHO Grade II glioma histology was the most common (54.3%), followed by Grades III (34.1%) and IV (11.6%). The median tumor volume was 48.5 cm3. The majority of insular gliomas were located in the anterior portion of the insula with 31.0% in Zone I, 10.9% in Zone IV, and 16.3% in Zones I+IV. The Berger-Sanai zone classification system was highly reliable, with a kappa coefficient of 0.857. The median EOR for all zones was 85%. Comparison of EOR between the current and prior series showed no change and Zone I gliomas continue to have the highest median EOR. Short- and long-term neurological complications remain low, and zone classification correlated with short-term complications, which were highest in Zone I and in Giant insular gliomas. CONCLUSIONS The previously proposed Berger-Sanai classification system is highly reliable and predictive of insular glioma EOR and morbidity.

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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 statistically analysed with Spearman and Mann–Whitney U tests and 21 features with statistically significant properties were selected from 107 features. The results showed that the best performing among the algorithms was Gaussian Naive Bayes algorithm with 80% accuracy. Machine learning and feature selection techniques can be used in the analysis of gliomas as well as pathological evaluations in glioma grading processes. Keywords: Radiomics, glioma, naive bayes.

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Billard, P., C. Guerriau, C. Carpentier, F. Juillard, N. Grandin, P. Lomonte, P. Kantapareddy, 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 (September 1, 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) assay was adapted to tumor (FFPE and frozen) samples. RESULTS We constructed a CC-based algorithm able to identify the TMM of 284 gliomas with either TERT or ATRX alteration, with a sensitivity of 100% and a specificity of 97.3%, and succeeded in deciphering the TMM involved in 122 aTMM gliomas. Additionally, the combination of the TMM, the mutational status of the Isocitrate dehydrogenase 1/2 (IDH) gene, and the histological grading was used as base for a new classification: TeloDIAG. Six subtypes are defined in this classification: tOD, tLGA, tGBM_IDHmt, tGBM, and tAIV, corresponding to oligodendroglioma, IDHmt low grade astrocytoma, IDHmt glioblastoma, and IDHwt glioblastoma, respectively, the last class gathers ALT+ IDHwt glioma. The TeloDIAG diagnosis is 99% concordant with the WHO classification for glioma displaying typical molecular characteristics (N=312). It modified the classification of 38% (N=156) discordant tumors, such as IDHwt Astrocytoma, aTMM tumors, or gliomas with unexpected TMM (e.g. TERTwt oligodendroglioma, ATRX loss GBM). Interestingly, 20% (N=69) of TERTwt, ATRXwt, or IDHwt GBM were actually tAIV, which is remarkable as tAIV-glioma patients’ survival tended to be longer (21.2 months) than tGBM patients’ survival (16.5 months). Importantly, CC in blood sampled from IDHmt astrocytoma patients was detected with a sensitivity of 56% and a specificity of 95% (N = 206). CONCLUSION In sum, the TeloDIAG is a new, simple, and efficient tool helping in glioma diagnosis and a promising option for liquid biopsy

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Im, Sanghyuk, Jonghwan Hyeon, Eunyoung Rha, Janghyeon Lee, Ho-Jin Choi, Yuchae Jung, and Tae-Jung Kim. "Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning." Sensors 21, no. 10 (May 17, 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 ResNet50V2 model was trained to classify subtypes and grades of diffuse gliomas according to the WHO’s new 2016 classification. The balanced accuracy of the diffuse glioma subtype classification model with majority voting was 0.8727. These results highlight an emerging role of deep learning in the future practice of pathologic diagnosis.

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Lewis, Paul D. "Classification of gliomas." Current Diagnostic Pathology 2, no. 3 (September 1995): 175–80. http://dx.doi.org/10.1016/s0968-6053(05)80056-0.

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Pisapia, David J. "The Updated World Health Organization Glioma Classification: Cellular and Molecular Origins of Adult Infiltrating Gliomas." Archives of Pathology & Laboratory Medicine 141, no. 12 (December 1, 2017): 1633–45. http://dx.doi.org/10.5858/arpa.2016-0493-ra.

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Context.— In the recently updated World Health Organization (WHO) classification of central nervous system tumors, our concept of infiltrating gliomas as a molecular dichotomy between oligodendroglial and astrocytic tumors has been codified. Advances in animal models of glioma and a wealth of sophisticated molecular analyses of human glioma tissue have led to a greater understanding of some of the biologic underpinnings of gliomagenesis. Objective.— To review our understanding of gliomagenesis in the setting of the recently updated WHO classification of central nervous system tumors. Topics addressed include a summary of an updated diagnostic schema for infiltrating gliomas, the crucial importance of isocitrate dehydrogenase mutations, candidate cells of origin for gliomas, environmental and other posited contributing factors to gliomagenesis, and the possible role of chromatin topology in setting the stage for gliomagenesis. Data Sources.— We conducted a primary literature search using PubMed. Conclusions.— With multidimensional molecular data sets spanning increasingly larger numbers of patients with infiltrating gliomas, our understanding of the disease at the point of surgical resection has improved dramatically and this understanding is reflected in the updated WHO classification. Animal models have demonstrated a diversity of candidates for glioma cells of origin, but crucial questions remain, including the role of neural stem cells, more differentiated progenitor cells, and glioma stem cells. At this stage the increase in data generated from human samples will hopefully inform the creation of newer animal models that will recapitulate more accurately the diversity of gliomas and provide novel insights into the biologic mechanisms underlying tumor initiation and progression.

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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 stress angiogénique tumoral. Nous avons mis en évidence des altérations de la structure secondaire des protéines (tels les collagènes) des vaisseaux sanguins au cours de la croissance de la tumeur. Nous avons aussi évalué les modifications des chaines d’acides gras des phospholipides membranaires, qui révélent un degré d’insaturation plus important pour les vaisseaux tumoraux. Ensuite, sur un modèle de gliome murin, nous avons établi une méthode efficace de classification des capillaires sanguins sur la base d’absorptions de leurs contenus glucidiques et lipidiques, permettant de discriminer totalement les capillaires sains et tumoraux. La combinaison de ces paramètres a été mise à profit pour assurer une histopathologie moléculaire des gliomes humains. Nos résultats ont démontré qu’il est possible de différencier entre la vasculature saine et tumorale sur ces gliomes humains, ce qui permet une bonne délimitation des zones tissulaires correspondantes. Cette technique pourrait devenir un outil analytique fiable, rapide d’une durée compatible avec la chirurgie et donc très utile pour les neurochirurgiens
Malignant 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

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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 may generate epileptic seizures. Alternatively, methods of functional brain imaging (e.g., PET, fMRI, MEG) may be applied, which allow individual localization of speech-eloquent areas. Matching of these image data with a conventional 3D-CT or MRI now allows the exact transfer of this information into the surgical field by neuronavigation. Whereas standards concerning electrophysiological stimulation techniques that could prevent a permanent postoperative worsening of language are available, until now it remains unclear whether the resection of regions shown to be active in functional brain imaging will cause a permanent postoperative deficit.
Die 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.

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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 quels que soient le grade et le groupe moléculaire de la tumeur (p < 0,0001). Nous avons établi une nouvelle stratégie diagnostique à partir de la classification moléculaire des gliomes en intégrant deux biomarqueurs pronostiques CHI3L1 et NTRK2. L’analyse multivariée confirme que ces biomarqueurs sont indépendants du statut IDH et du grade tumoral. Si nous avons mis en évidence par l’analyse protéique de CHI3L1 une concordance avec les transcrits, les résultats divergent pour TrkB. Ainsi, une expression élevée de TrkB et son corécepteur p75NTR serait liée à l’agressivité tumorale indépendamment du statut IDH. Enfin, TrkB et p75NTR sont présents aussi bien dans les exosomes issus du plasma de témoins sains et de patients atteints de gliomes mais leur expression augmente en fonction de l’agressivité de la tumeur
Gliomas, 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

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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’identifier de nouvelles thérapies pouvant améliorer le pronostic des patients. La classification OMS 2016 des tumeurs du système nerveux central a, pour la première fois, intégré les données de biologie moléculaires aux données histopathologiques, afin d’améliorer la distinction des différents sous-groupes de tumeurs et d’orienter au mieux les choix thérapeutiques pour chaque sous-groupe.Nous nous sommes intéressés dans ce travail à l’apport de l’approche en protéomique par imagerie par matrix-assisted laser desorption/ionization spectrométrie de masse MALDI (MALDI-MSI) couplée à l’analyse en microprotéomique dans les gliomes dans le cadre de l’étude clinique GLIOMIC (NCT02473484) qui a pour but de réaliser une classification moléculaire des gliomes en intégrant les données cliniques et celles obtenues par ces nouvelles approches.La faisabilité de la technique a d’abord été validée sur une série de gliomes anaplasiques. Dans cette première analyse, nous avons pu démontrer que, bien que l’approche protéomique confirmait également l’hétérogénéité tumorale, les analyses histologiques et protéomiques divergent et apportent des informations complémentaires. L’imagerie moléculaire protéomique a mis en évidence trois différents groupes d’expression de protéines : un groupe de protéines associé au cancer, un groupe de protéines impliquées dans l’inflammation et un groupe de protéines impliquées dans la différentiation des cellules nerveuses et la croissance des neurites.Nous nous sommes ensuite intéressés aux glioblastomes. Les premiers résultats ont également confirmés l’existence des 3 régions d’intérêt définies sur le plan moléculaires, apportant de nouvelles informations par rapport aux données histopathologiques. Ces résultats doivent être confirmés dans une cohorte plus large de patients.En conclusion, l’intégration de ces biomarqueurs protéomiques, aux données cliniques, histopathologiques et de biologie moléculaire, pourrait permettre d’améliorer les connaissances sur les gliomes, leur classification et l’identification de nouvelles cibles thérapeutiques potentielles
The 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

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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 outline aspects of molecular testing in relation to integrated diagnosis under the WHO 2016 Classification, with the recognition of the more favourable tumours containing IDH mutation (Chapter 2); and an approach to modify radiation therapy based on the presence of IDH mutation and radiological features including PET imaging to improve target volume delineation and minimise late morbidity (Chapter 3). The next two chapters demonstrate the response of these IDH mutated anaplastic glioma to radiation therapy and chemotherapy demonstrating the excellent reduction in radiological volume (Chapter 4), and also the unusual early pattern of failure post treatment based of a subgroup of IDH mutated tumours compared to IDH wild type tumours (Chapter 5). The final two chapters describe the outcome of treatment in regards to favourable six-year progression free survival which should translate to a high ten-year overall survival (Chapter 6). The improved survival is also demonstrated in Chapter 7 to be associated with a high level of functioning at three to five years post treatment with minimal negative impact from therapy.

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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 permettant de caractériser les systèmes biologiques. La spectrométrie RMN haute résolution en rotation à l'angle magique (HRMAS) est la technique que nous avons choisie pour l'étude du métabolome des tumeurs car elle regroupe un nombre important d'avantages (simplicité de préparation des échantillons, la reproductibilité intra et inter laboratoire et le cout relativement modeste de fonctionnement). L'étude principale réalisée dans le cadre de cette thèse a porté sur les tumeurs cérébrales. Une centaine de tumeurs ont ainsi été analysé et leur métabolome caractérisé. L'analyse effectuée s'est focalisé sur la problématique du « grading de malignité » des tumeurs cérébrales, principalement sur les gliomes. Ainsi une forte corrélation a été trouvé entre les profiles métabolique des gliomes (glioblastome, oligodendrogliome) et l'évolution clinique des patients. L'approche métabolomique pour l'étude des tumeurs, d'un point de vue diagnostic et pronostic, semble aujourd'hui prometteuse. De plus l'étude des profiles métaboliques, devrait, à terme, permettre une meilleure compréhension des perturbations métaboliques lié à la pathologie, et potentiellement, conduire à la mise en place de traitements individualisés parfaitement adaptés à chaque patient. Néanmoins, une étape de validation de ces premières observations est aujourd'hui indispensable. C'est pourquoi ces travaux ont accompagné l'installation du premier spectromètre RMN HRMAS dans le milieu clinique en novembre 2007 dans le cadre du projet CARMeN. L'objectif général de ce projet est, maintenant, d'étendre l'expérience acquise durant cette thèse à tous les domaines de la cancérologie où il existe, soit une dissociation entre le diagnostic histopathologique de la tumeur et le devenir du patient, soit une discordance à la réponse thérapeutique pour un sous‐type de tumeur.

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Crespin, 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.

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La communication intercellulaire par les jonctions gap (CIJG) a été proposée comme l’un des éléments impliqués dans la cancérogenèse très rapidement après sa mise en évidence, dans les années 1960. Ainsi l’induction de l’expression de connexines, motif structural de base de la CIJG, a été décrite comme étant capable de « normaliser » le phénotype de cellules cancéreuses. Notre étude de la connexine 43 (Cx43), par tissue micro array, dans des tumeurs gliales humaines (59 échantillons) a montré une délocalisation et une perte de l’expression de la protéine. La situation s’avère complexe par l’hétérogénéité intratumorale; en effet, certaines cellules du tissu tumoral montrent un signal avec une localisation aberrante dans le cytoplasme ou dans le noyau. Certains travaux ayant suggéré que Cx43 pourrait « normaliser » le phénotype tumoral par une action indépendante de la CIJG, Cx43 ou des formes tronquées de la protéine ont été exprimées par des vecteurs rétroviraux dans des lignées de tumeurs gliales humaines. Les résultats obtenus ont suggéré que l’expression de la protéine ne permettait pas de réduire le potentiel prolifératif des cellules tumorales lorsque celles-ci sont maintenues en monocouche. En revanche, la capacité des cellules à proliférer sans ancrage est réduite par l’expression de Cx43 mais aussi par des formes tronquées de la protéine ne permettant pas la CIJG. De plus, les cellules exprimant Cx43, entière ou tronquée, apparaissent douées d’une plus grande motilité. En conclusion, Cx43 semble jouer un rôle complexe dans la progression des tumeurs gliales humaines, celle-ci apparaissant avec des localisations aberrantes dont l’effet demeure inconnu. L’expression de la Cx43 ne constituerait pas nécessairement un facteur de bon pronostic, car si les cellules montrent une diminution de leur prolifération dans un environnement défavorable, elles semblent, en revanche, plus aptes à migrer, ce qui permettrait l’invasion du tissu environnant
The 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

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Books on the topic "Gliomas classification"

1

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 anaplastic astrocytoma. Medulloblastomas are now defined by combining histological patterns (classic, desmoplastic/nodular, extensive nodularity, anaplastic) and genetic hallmarks (WNT-activated; SHH-activated, TP53-mutant; SHH-activated, TP53-wildtype; non-WNT/non-SHH). Other newly recognized tumour entities include diffuse midline glioma, H3 K27M-mutant; ependymoma, RELA fusion-positive; and embryonal tumour with multilayered rosettes. The new classification is a significant step forward and will facilitate the development of novel targeted therapies of brain tumours.

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David N., M.D. Louis (Editor), Hiroko Ohgaki (Editor), Otmar D. Wiestler (Editor), and Webster K. Cavenee (Editor), eds. Who Classification of Tumours of the Central Nervous System (Who Classfication of Tumours). 4th ed. Not Avail, 2007.

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Book chapters on the topic "Gliomas classification"

1

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.

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Kato, 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.

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Rigau, 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.

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Allinson, 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.

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Rigau, 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.

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Purkait, 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.

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Quinones, Addison, and 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.

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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 therapies for GBM have shown little success as the disease is almost always lethal; however, researchers are making progress and learning how to combine therapeutic strategies most effectively. GBMs can be classified initially into two subsets consisting of primary and secondary GBMs, and this categorization stems from cancer development. GBM is the highest grade of gliomas, which includes glioma I (low proliferative potential), glioma II (low proliferative potential with some capacity for infiltration and recurrence), glioma III (evidence of malignancy), and glioma IV (GBM) (malignant with features of necrosis and microvascular proliferation). Secondary GBM develops from a low-grade glioma to an advanced-stage cancer, while primary GBM provides no signs of progression and is identified as an advanced-stage glioma from the onset. The differences in prognosis and histology correlated with each classification are generally negligible, but the demographics of individuals affected and the accompanying genetic/metabolic properties show distinct differentiation [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, 73–100. Cham: 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, 61–72. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-2213-5_5.

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Johnson, Derek R., Caterina Giannini, and 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.

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Conference papers on the topic "Gliomas classification"

1

Ul Ain, Qurat, Iqra Duaa, Komal Haroon, Faisal Amin, and 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.

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Kounelakis, M. G., M. E. Zervakis, G. C. Giakos, C. Narayan, S. Marotta, D. Natarajamani, G. J. Postma, L. M. C. Buydens, and 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.

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van der Voort, Sebastian R., Renske Gahrmann, Martin J. van den Bent, Arnaud J. P. E. Vincent, Wiro J. Niessen, Marion Smits, and 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.

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Grzegorzek, Marcin, Marianna Buckan, and Sigrid Horn. "Probabilistic classification of intracranial gliomas in digital microscope images based on EGFR quantity." In SPIE Medical Imaging, edited by Josien P. W. Pluim and Benoit M. Dawant. SPIE, 2009. http://dx.doi.org/10.1117/12.811552.

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Tursynbek, Nurislam, Ghazal Ghahramany, Sheida Nabavi, and Amin Zollanvari. "Predictive Meta-analysis of Multiple Microarray Datasets: An Application to Classification of Malignant Gliomas." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621503.

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Steiner, Gerald, R. A. Shaw, Lin-P'ing Choo-Smith, Wolfram Steller, Laryssa Shapoval, Gabriele Schackert, Stephan Sobottka, Reiner Salzer, and Henry H. Mantsch. "Detection and grading of human gliomas by FTIR spectroscopy and a genetic classification algorithm." In International Symposium on Biomedical Optics, edited by Anita Mahadevan-Jansen, Henry H. Mantsch, and Gerwin J. Puppels. SPIE, 2002. http://dx.doi.org/10.1117/12.460789.

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Cipriano, Carolina L. S., Giovanni L. F. Da Silva, Jonnison L. Ferreira, Aristófanes C. Silva, and 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.

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One of the most severe and common brain tumors is gliomas. Manual classiﬁcation of injuries of this type is a laborious task in the clinical routine. Therefore, this work proposes an automatic method to classify lesions in the brain in 3D MR images based on superpixels, PSO algorithm and convolutional neural network. The proposed method obtained results for the complete, central and active regions, an accuracy of 87.88%, 70.51%, 80.08% and precision of 76%, 84%, 75% for the respective regions. The results demonstrate the difﬁculty of the network in the classiﬁcation of the regions found in the lesions.

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Felipe, Caio dos Santos, Thatiane Alves Pianoschi Alva, Ana Trindade Winck, and 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.

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Brain tumor diagnosis is a complex problem that requires specialized skills and knowledge. Manual analysis is often time-consuming, and there can be high subjectivity in interpreting results. Convolutional neural networks (CNNs) have emerged as a promising solution for automatically classifying brain tumors from magnetic resonance images (MRI). CNNs are a type of neural network that can automatically learn and extract relevant features from images, making them particularly suited to this task when applied in deep learning algorithms. The use of CNNs for brain tumor diagnosis has been widely explored in the literature, with many studies reporting promising results. By leveraging datasets of labeled MRI, CNNs can learn to accurately detect and classify different types of brain tumors, including gliomas, meningiomas, and pituitary adenomas. These models have been shown to outperform traditional machine-learning algorithms and even human experts in some cases. This article presents a CNN model designed to identify and classify brain tumors from MRI. The model was trained on a large dataset of MRI, and its performance was evaluated on an independent test set. The model achieved an accuracy of 99% considering all validation steps and outperformed state-of-the-art methods for brain tumor classification. When considering individual classes, the accuracy percentages were 100%, 98%, 99%, and 99% for glioma, meningioma, notumor, and pituitary, respectively. The development of accurate and efficient methods for brain tumor diagnosis is critical for improving patient outcomes and reducing healthcare costs. This article can advance our understanding of leveraging these powerful algorithms best to solve real-world healthcare problems by contributing to the growing literature on deep learning for medical image analysis.

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Kong, Jun, Lee Cooper, Fusheng Wang, Candace Chisolm, Carlos Moreno, Tahsin Kurc, Patrick Widener, Daniel Brat, and Joel Saltz. "A comprehensive framework for classification of nuclei in digital microscopy imaging: An application to diffuse gliomas." In 2011 8th IEEE International Symposium on Biomedical Imaging (ISBI 2011). IEEE, 2011. http://dx.doi.org/10.1109/isbi.2011.5872833.

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Chakrabarty, Satrajit, Pamela LaMontagne, Joshua Shimony, Daniel S. Marcus, and Aristeidis Sotiras. "Non-invasive classification of IDH mutation status of gliomas from multi-modal MRI using a 3D convolutional neural network." In Computer-Aided Diagnosis, edited by Khan M. Iftekharuddin and Weijie Chen. SPIE, 2023. http://dx.doi.org/10.1117/12.2651391.

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