Auswahl der wissenschaftlichen Literatur zum Thema „Peritumoral area“
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Zeitschriftenartikel zum Thema "Peritumoral area"
Tabacaru, Gigi, Simona Moldovanu und Marian Barbu. „Algorithm for Analyzing the Microenvironment Surrounding Melanoma“. SYSTEM THEORY, CONTROL AND COMPUTING JOURNAL 3, Nr. 2 (31.12.2023): 15–19. http://dx.doi.org/10.52846/stccj.2023.3.2.52.
Der volle Inhalt der QuelleLy, Ina, Barbara Wichtmann, Susie Yi Huang, Aapo Nummenmaa, Ovidiu Andronesi, Qiuyun Fan, William T. Curry et al. „Characterizing glioma microenvironment with ultra-high gradient diffusion MRI.“ Journal of Clinical Oncology 35, Nr. 15_suppl (20.05.2017): 2050. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.2050.
Der volle Inhalt der QuelleKumala Wardani, Bestia, Yuyun Yueniwati und Agus Naba. „The Application of Image Segmentation to Determine the Ratio of Peritumoral Edema Area to Tumor Area on Primary Malignant Brain Tumor and Metastases through Conventional Magnetic Resonance Imaging“. Open Access Macedonian Journal of Medical Sciences 10, B (01.01.2022): 26–30. http://dx.doi.org/10.3889/oamjms.2022.7777.
Der volle Inhalt der QuelleTatagiba, Marcos, Shahram Mirzai und Madjid Samii. „Peritumoral Blood Flow in Intracranial Meningiomas“. Neurosurgery 28, Nr. 3 (01.03.1991): 400–404. http://dx.doi.org/10.1227/00006123-199103000-00010.
Der volle Inhalt der QuelleGao, Xiang, Haiyan Wang, Shanbao Cai, M. Reza Saadatzadeh, Helmut Hanenberg, Karen E. Pollok, Aaron A. Cohen-Gadol und Jinhui Chen. „Phosphorylation of NMDA 2B at S1303 in human glioma peritumoral tissue: implications for glioma epileptogenesis“. Neurosurgical Focus 37, Nr. 6 (Dezember 2014): E17. http://dx.doi.org/10.3171/2014.9.focus14485.
Der volle Inhalt der QuelleZhao, Xinyao, Qingqing Wen, Junying Wang, Weiqiang Dou, Guowei Zhang und Hao Shi. „Is intravoxel incoherent motion magnetic resonance imaging useful for predicting hepatocellular cancer recurrence and invasion of the peritumoral zone after transarterial chemoembolization?“ Journal of Cancer Research and Therapeutics 20, Nr. 2 (April 2024): 584–91. http://dx.doi.org/10.4103/jcrt.jcrt_1582_23.
Der volle Inhalt der QuelleDanilova, N. V., V. M. Kkomyakov, A. V. Chayka, I. A. Mikhailov, N. A. Oleynikova und P. G. Malkov. „CHARACTERISTICS OF THE IMMUNE MICROENVIRONMENT OF THE NORMAL MUCOUS MEMBRANE OF THE PERITUMORAL AREA IS AN ADDITIONAL INDEPENDENT PROGNOSTIC FACTOR IN GASTRIC CANCER“. Siberian journal of oncology 20, Nr. 1 (06.03.2021): 74–86. http://dx.doi.org/10.21294/1814-4861-2021-20-1-74-86.
Der volle Inhalt der QuelleBoia, Eugen Radu, Simina Boia, Raluca Amalia Ceausu, Pusa Nela Gaje, Sarrah Mariam Maaroufi, Florica Sandru und Marius Raica. „The Follicular Dendritic Cells and HPV 18 Interrelation in Head and Neck Squamous Cell Carcinomas of the Larynx“. Medicina 59, Nr. 6 (02.06.2023): 1072. http://dx.doi.org/10.3390/medicina59061072.
Der volle Inhalt der QuelleLee, Hongseok, Kyungdoc Kim, Guhyun Kang, Kyu-Hwan Jung und Sunyoung S. Lee. „Abstract 1721: Spatial distribution of immune cells as quantitative prognosis indicator in hepatocellular carcinoma“. Cancer Research 82, Nr. 12_Supplement (15.06.2022): 1721. http://dx.doi.org/10.1158/1538-7445.am2022-1721.
Der volle Inhalt der QuelleSamani, Zahra Riahi, Drew Parker, Jacob Antony Alappatt, Steven Brem und Ragini Verma. „NIMG-21. DIFFERENTIATING TUMOR TYPES BASED ON THE PERITUMORAL MICROENVIRONMENT USING CONVOLUTIONAL NEURAL NETWORKS“. Neuro-Oncology 22, Supplement_2 (November 2020): ii151. http://dx.doi.org/10.1093/neuonc/noaa215.634.
Der volle Inhalt der QuelleDissertationen zum Thema "Peritumoral area"
Michot, Audrey. „Projet PériSARC : identification de facteurs morphologiques et moléculaires prédictifs de rechute dans les sarcomes des tissus mous des membres et de la paroi du tronc“. Electronic Thesis or Diss., Bordeaux, 2023. http://www.theses.fr/2023BORD0239.
Der volle Inhalt der QuelleIdentification of morphological and molecular predictive factors of recurrence in soft tissue sarcomas of the limbs and trunk wallSarcomas are rare malignant mesenchymal tumors that represent 2/100,000 new cases per year. They represent a heterogeneous group of tumors, generally associated with a poor prognosis. Surgical resection remains the only curative treatment. These tumors can recur even after optimal surgery, classified R0, i.e. without microscopic residue (in about 10% of cases). Different pathological prognostic factors are known, including initial tumor size, FNCLCC histological grade scheme and deep tumor location. At present, there is no available marker to predict the risk of recurrence following curative surgical resection of a sarcoma, which complicates the clinical decision making.So far, studies have focused on the features of tumor cells but the features of tissue margins surrounding sarcoma cells are unknown. The margin constitutes the interface between the tumor tissue and the healthy tissue, it is a remodeled tissue located in direct contact with the tumor cells. The mode of tumor cell infiltration differs from one tumor to another and could modulate or reflect the risk of local relapse. Interestingly, a molecular signature predictive of local relapse has been identified in hepatocellular carcinoma by studying of the “healthy tissue”, but no data is available in sarcomas. This thesis project aims at identifying predictive factors of local tumor relapse by systematically and comparatively studying the tumor and its peripheral zones, including the central tumor zone (Tc), peripheral tumor zone (Tp), healthy peritumoral tissue in contact tumor cells (HT-R1) and remote healthy tissue (HT) in a retrospective series of 144 soft tissue sarcomas.In a first part, we have characterized the immune infiltrate associated with sarcomas by systematically studying the tissue margin with a focus on tertiary lymphoid structures (TLS), a predictive factor of response to immunotherapy in sarcomas using microscopy and immunohistochemistry. The distribution of TLS predominates in the tumor margin and seems to be correlated with a better prognosis.The second part studied by deep learning (DL) the different tumor areas using scanned slides to highlight new unknown predictive morphological factors correlated with relapse and to establish a predictive Deep learning (DL) signature of local relapse. The DL score exceeds the survival curves associated with the FNCLCC grade; the current gold standard used to assess patient risk in clinical practice.A third approach compared the transcriptome of the different tumor areas determined by whole RNA-sequencing to identify deregulated biomarkers in the different areas and to determine the immunological signatures in the different territories. Genes of interest have been highlighted in the marginal zone that may be associated with the risk of relapse. Finally, a spatial transcriptomic analysis by Visium technique was carried out on a few cases on an exploratory basis.Our study has evidenced biomarkers correlated with a higher risk of relapse that could allow in the future to identify high risk patients to personalize their follow-up and validate complementary therapeutic modalities
Ting, Yi-Cen, und 丁怡岑. „Microstructural Characterization in the Peritumoral Area of Glioma Patients and in the Corpus Callosum of Normal Subjects Using Neurite Orientation Dispersion and Density Imaging (NODDI)“. Thesis, 2018. http://ndltd.ncl.edu.tw/handle/hj8ra4.
Der volle Inhalt der Quelle國立陽明大學
腦科學研究所
107
Diffusion magnetic resonance imaging (dMRI) is a technique for the non-invasive characterization of the microstructure in biological tissues since it is sensitive for the diffusion processes of hydrogen molecules. dMRI is a promising candidate for in vivo quantification of neurite morphology in white matter. Over the past two decades, conventional dMRI method usually focused on Diffusion Tensor Imaging (DTI). DTI was widely used to assess the organization of tissue in white matter, providing some indices to describe changes in biology. The model of DTI describes the diffusive water molecules relevant to free diffusion or hindered anisotropic diffusion homogeneous within each voxel based on the assumption of Gaussian distribution. However, DTI was obtained at single b-value and lacked of specificity for describing tissues in this assumption. Additionally, several advanced dMRI techniques, especially multi-compartment models, have been proposed with complicated assumption for estimating neuron morphology. Neurite Orientation Dispersion and Density Imaging (NODDI) is a clinically feasible technique for estimating the microstructural complexity in central neuron system imaging, post by Zhang et al. in 2012. NODDI is a multi-compartment tissue model based on dMRI, combining a three-compartment tissue model: restricted compartment for non-Gaussian anisotropic diffusion (referring to the space bounded by the membrane of neurites), hindered compartment for Gaussian anisotropic diffusion (referring to the space around the neurites) and isotropic compartment for Gaussian diffusion (referring to the CSF space) in each voxel. Using three compartments, NODDI map not only axons in the white matter but also dendrites in gray matter in each voxel. Compared to DTI indices, NODDI may provide greater specificity to morphology and pathology, e.g. neurite density and orientation dispersion. The aims of this work are to explore the promising indices of diffusion models in characterizing the microstructural complexity in the peritumoral area of gliomas and to show the clinical feasibility and potential capability of NODDI studies. The first chapter gave an overview of dMRI and explained the models of NODDI as well as DTI (Chapter 1). In the chapters 2~5, we investigated the different preprocessing interference on NODDI and DTI as verified by topography of corpus callosum for optimization (Chapter 2), and then we differentiated different types of gliomas and characterized the infiltration in peritumoral area by NODDI and DTI using the optimized preprocessing method (Chapter 3); furthermore, we in-vivo evaluated the simplified NODDI imaging protocol and constructed the semi-automatic regions of interest (ROI) delineation for peritumoral areas (Chapter 4 and 5). Finally, the last chapter gave a conclusion of this thesis (Chapter 6).
Bücher zum Thema "Peritumoral area"
Hatef, Jeffrey, und Russell R. Lonser. Hemangioblastoma. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190696696.003.0007.
Der volle Inhalt der QuelleWoolf, Eric C., und Adrienne C. Scheck. Ketogenic Diet as Adjunctive Therapy for Malignant Brain Cancer. Herausgegeben von Jong M. Rho. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190497996.003.0013.
Der volle Inhalt der QuelleBuchteile zum Thema "Peritumoral area"
Chassoux, Francine, Elisabeth Landré und Bertrand Devaux. „Invasive EEG in Tumoural Epilepsy“. In Invasive Studies of the Human Epileptic Brain, herausgegeben von Samden D. Lhatoo, Philippe Kahane und Hans O. Lüders, 198–212. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198714668.003.0016.
Der volle Inhalt der QuelleS. Ahmed, Sunbul. „Corticosteroids in Neuro-Oncology: Management of Intracranial Tumors and Peritumoral Edema“. In Corticosteroids - A Paradigmatic Drug Class. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.100624.
Der volle Inhalt der QuelleScheck, Adrienne C., und Nelofer Syed. „Ketogenic Diet as Adjunctive Therapy for Malignant Brain Cancer“. In Ketogenic Diet and Metabolic Therapies, herausgegeben von Susan A. Masino, Detlev Boison, Dominic P. D’Agostino, Eric H. Kossoff und Jong M. Rho, 125–53. Oxford University Press, 2022. http://dx.doi.org/10.1093/med/9780197501207.003.0015.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Peritumoral area"
Andrade, Danúbia Ariana de, Filomena Marino Carvalho, Fernando Nalesso Aguiar, Alfredo Luiz Jacomo und Alfredo Carlos Simões Dornellas de Barros. „SIZE OF METASTATIC INFILTRATION IN THE SENTINEL NODE AS A PREDICTOR OF NON‑SENTINEL NODES INVOLVEMENT“. In Scientifc papers of XXIII Brazilian Breast Congress - 2021. Mastology, 2021. http://dx.doi.org/10.29289/259453942021v31s1065.
Der volle Inhalt der QuelleSebiskveradze, David, Cyril Gobinet, Nathalie CARDOT-LECCIA, Jean-Paul ORTONNE, Michel MANFAIT, Pierre JEANNESSON und Olivier PIOT. „Abstract 4052: Highlighting intratumoral heterogeneity and peritumoral areas in human melanoma biopsies by infrared spectral microimaging“. In Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL. American Association for Cancer Research, 2012. http://dx.doi.org/10.1158/1538-7445.am2012-4052.
Der volle Inhalt der QuelleFonseca, Felipe Cordeiro, Eduardo Carvalho Pessoa, Carla Priscila Kamyia Pessoa, Benedito Sousa Almeida Filho und Heloisa Maria De Luca Vespoli. „STUDY OF MORPHOLOGICAL AND ANGIOGENIC FEATURES OF TRIPLE NEGATIVE TUMORS BY ULTRASONOGRAPHY“. In Scientifc papers of XXIII Brazilian Breast Congress - 2021. Mastology, 2021. http://dx.doi.org/10.29289/259453942021v31s1012.
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