Relevant bibliographies by topics / Tumors classification

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Tumors classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Tumors classification"

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Dozic, Slobodan, Dubravka Cvetkovic-Dozic, Milica Skender-Gazibara, and Branko Dozic. "Review of the World Health Organization classification of tumors of the nervous system." Archive of Oncology 10, no. 3 (2002): 175–77. http://dx.doi.org/10.2298/aoo0203175d.

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(Conclusion) Classifications of the nervous system tumors should be neither static nor definitive. The most recent, third, current WHO classification of nervous system tumors was published in 2000. Many substantial changes were introduced. New entities include the chordoid glioma of the third ventricle, the atypical teratoid/rhabdoid tumor, cerebellar liponeurocytoma (the former lipomatous medulloblstoma of the cerebellum), solitary fibrous tumor and perineurioma. The new tumor variants include the large cell medulloblastoma, tanacytic ependymoma and rhabdoid meningioma. Several essential changes were introduced in the meningiomas regarding histological subtypes, grading and proliferation index. In addition to new entities described in the 2000 WHO classification there are newly brain tumor entities and tumor variants, which are not included in the current classification due to the insufficient number of reporeted cases, for example papillary glioneuronal tumor, rosetted glioneuronal tumor, lipoastrocytoma and lipomatous meningioma. They will be probably accepted in the next WHO classificaton. In the current WHO classification the importance of cytogenetic and molecular biologic investigation in the understanding and further classifications of these tumors has been emphasized.
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Zhang, Zhiyu, Yingqi Hua, Guodong Li, Wei Sun, Shuo Hu, Jian Li, and Zhengdong Cai. "Preliminary proposal for surgical classification of sacral tumors." Journal of Neurosurgery: Spine 13, no. 5 (November 2010): 651–58. http://dx.doi.org/10.3171/2010.5.spine09443.

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ObjectThe authors propose a new surgical classification method for sacral tumors that improves the guidance for specific surgical decisions and approaches.MethodsThe authors retrospectively studied the clinical courses of 92 patients with sacral tumors treated at the Changhai Hospital; all patients underwent tumor resection between January 2000 and August 2005. The clinical characteristics, imaging features, and pathological classifications were carefully assessed in each case. The tumors were classified according to the imaging features and intraoperative findings. The surgical approach and the resection area were determined according to the tumor classification.ResultsThe proposed surgical classification system divided the sacral tumors into 2 major types according to the lesion's anatomical position in the sagittal plane. The tumors were further divided into 4 subtypes according to the length of the tumor's anterior protrusion into the pelvic cavity. Finally, each tumor subtype was classified into 16 areas according to the anatomical position in the cross-sectional plane. This classification method was used to categorize the sacral tumors, all of which were totally resected under the naked eye. Postoperatively symptoms were improved to varying degrees.ConclusionsThe appropriate classification of sacral tumors and the selection of a corresponding surgical approach can improve the rate of total resection and the surgical safety, as well as decrease the recurrence rate.
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Park, Sung-Hye, Jae Kyung Won, Chi Heon Kim, Ji Hoon Phi, Seung-Ki Kim, Seung Hong Choi, and Chun Kee Chung. "Pathological Classification of the Intramedullary Spinal Cord Tumors According to 2021 World Health Organization Classification of Central Nervous System Tumors, a Single-Institute Experience." Neurospine 19, no. 3 (September 30, 2022): 780–91. http://dx.doi.org/10.14245/ns.2244196.098.

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According to the new 2021 World Health Organization (WHO) classification of tumors of the central nervous system (CNS) the classification of the primary intramedullary spinal cord tumors (IM-SCT) follows that of CNS tumors. However, since the genetics and methylation profile of ependymal tumors depend on the location of the tumor, the ‘spinal (SP)’ should be added for the ependymoma (EPN) and subependymoma (SubEPN). For an evidence-based review, the authors reviewed SCTs in the archives of the Seoul National University Hospital over the past decade. The frequent pathologies of primary IM-SCT were SP-EPN (45.1%), hemangioblastoma (20.0%), astrocytic tumors (17.4%, including pilocytic astrocytoma [4.6%] and diffuse midline glioma, H3 K27-altered [4.0%]), myxopapillary EPN (11.0%), and SP-subEPN (3.0%) in decreasing order. IDH-mutant astrocytomas, oligodendrogliomas, glioneuronal tumors, embryonal tumors, and germ cell tumors can occur but are extremely rare in the spinal cord. Genetic studies should support for the primary IM-SCT classification. In the 2021 WHO classifications, extramedullary SCT did not change significantly but contained several new genetically defined types of mesenchymal tumors. This article focused on primary IM-SCT for tumor frequency, age, sex difference, pathological features, and genetic abnormalities, based on a single-institute experience.
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Gupta, Anshu, and Tanima Dwivedi. "A Simplified Overview of World Health Organization Classification Update of Central Nervous System Tumors 2016." Journal of Neurosciences in Rural Practice 08, no. 04 (October 2017): 629–41. http://dx.doi.org/10.4103/jnrp.jnrp_168_17.

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ABSTRACTAfter 8 years, an update of central nervous system (CNS) tumors was published in 2016 after 2007. First time ever, molecular markers along with histology have been used in classification of any tumor. Major changes are seen in glioma and medulloblastoma groups. Few entities have been added such as diffuse midline glioma, H3 K27M-mutant, RELA fusion-positive ependymoma, embryonal tumor with multilayered rosettes, C19MC-altered, and hybrid nerve sheath tumors. Few variants and patterns that no longer have diagnostic and/or biological relevance and have been deleted such as glioblastoma cerebri, protoplasmic and fibrillary astrocytoma, and cellular ependymoma. Other changes include deletion of term “primitive neuroectodermal tumor,” addition of criterion of brain invasion in atypical meningioma, separation of melanotic schwannoma from other schwannoma, and combination of solitary fibrous tumors and hemangiopericytoma as one entity. There is also expansion of entities in nerve sheath tumors and hematopoietic/lymphoid tumors of the CNS. In this review article, we tried to review CNS tumors 2016 classification update in a simplified manner; comparing the differences between 2016 and 2007 CNS tumors classifications with brief description of important molecular markers and finally utility as well as challenges of this classification.
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Vladova, Paulina T. "Classification of Gastroenteropancreatic Neuroendocrine Tumors." Journal of Biomedical and Clinical Research 15, no. 2 (December 1, 2022): 123–29. http://dx.doi.org/10.2478/jbcr-2022-0017.

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Summary Gastroenteropancreatic neuroendocrine tumors are a heterogeneous group of tumors. There are several classification systems, and all of them have been validated.The article aims to summarize the existing classification systems of gastroenteropancreatic neuroendocrine tumors.A critical evaluation was based on the data available from existing studies.The classification of the European neuroendocrine tumor society is the one with the clinical benefits.The lack of unified classification systems creates incomplete epidemiologic data, leading to confusion among pathologists and clinicians.
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Bertoni, Franco, and Patrizia Bacchini. "Classification of bone tumors." European Journal of Radiology 27 (May 1998): S74—S76. http://dx.doi.org/10.1016/s0720-048x(98)00046-1.

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Ackermann, Sandra, Maria Cartolano, Barbara Hero, Anne Welte, Yvonne Kahlert, Andrea Roderwieser, Christoph Bartenhagen, et al. "A mechanistic classification of clinical phenotypes in neuroblastoma." Science 362, no. 6419 (December 6, 2018): 1165–70. http://dx.doi.org/10.1126/science.aat6768.

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Neuroblastoma is a pediatric tumor of the sympathetic nervous system. Its clinical course ranges from spontaneous tumor regression to fatal progression. To investigate the molecular features of the divergent tumor subtypes, we performed genome sequencing on 416 pretreatment neuroblastomas and assessed telomere maintenance mechanisms in 208 of these tumors. We found that patients whose tumors lacked telomere maintenance mechanisms had an excellent prognosis, whereas the prognosis of patients whose tumors harbored telomere maintenance mechanisms was substantially worse. Survival rates were lowest for neuroblastoma patients whose tumors harbored telomere maintenance mechanisms in combination with RAS and/or p53 pathway mutations. Spontaneous tumor regression occurred both in the presence and absence of these mutations in patients with telomere maintenance–negative tumors. On the basis of these data, we propose a mechanistic classification of neuroblastoma that may benefit the clinical management of patients.
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Lukianchenko, A. B., B. M. Medvedeva, A. I. Karseladze, and K. A. Romanova. "Morphology classification of liver tumors (comparison of the last and previous WHO classifications 2010 and 2019)." Medical Visualization 24, no. 2 (June 18, 2020): 138–43. http://dx.doi.org/10.24835/1607-0763-2020-2-138-143.

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Some changes and details of the last WHO liver tumors classification (2019) in comparison with the previous WHO classification (2010) are being discussed. Using different methods of investigations allow us to get better understanding of pathologic processes and their evolution. It is highly recommended to use WHO classification of tumours. 5th Edition. Digestive System Tumours. Еdited by the WHO Classification of Tumours Editorial Board. Lyon, IARC Press, 2019 in everyday clinical practice and scientific activity.
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Presl, Jiří, Tomáš Vaněček, Michael Michal, Jiří Bouda, Jan Kosťun, Pavel Vlasák, and Petr Stráník. "Molecular classification of endometrial cancers translated into practice." Česká gynekologie 86, no. 4 (August 30, 2021): 258–62. http://dx.doi.org/10.48095/cccg2021258.

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Summary: Objective: The main objective of the article is to clearly inform healthcare professionals about the newly implemented molecular classification of endometrial cancer into practice. Methods: Summary of current knowledge, recommendations and new procedures relating to molecular genetic examination of the tissues of patients with endometrial carcinoma. Results: Endometrial cancer is currently diagnosed on the base of histopathological morphology. According to the classical Bokhman division, we distinguish between two relatively wide groups of tumors which are different in pathogenesis: type I – estrogen-dependent tumors, clinically usually indolent, and type II – non-endometroid tumors, clinically aggressive, without dependence on estrogen stimulation. This classification fulfills a didactic purpose and provides easy orientation for epidemiological data, but is not suitable for stratification due to the overlap of clinical, pathological and molecular features. The Cancer Genome Atlas project classifies endometrial tumors into 4 groups based on molecular genetic features. Conclusion: Integration of the histopathological findings along with molecular classification appears to be the best approach for evaluating each individual tumor. This will help to achieve the ideal stratifi cation of patients for treatment regimens.
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Pol, Jay. "Brain Tumor Image Classification using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1934–41. http://dx.doi.org/10.22214/ijraset.2022.44191.

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Abstract: We present a method for segmenting and categorizing brain tumors in the challenge of content of brain tumor with segmentation is enrolled and skull is exposed for bar graph equivalent high-level contradiction refer amount. Preprocessing, segmentation, feature extraction, optimization, and classification are used to detect tumors. The tissue is then classified using preprocessed images. We utilized leave-one-out cross-validation to generate a Dice overlap of 88 for the whole tumor area, 75 for the core tumor region, and 95 for the enhancing tumor region, which is higher than the Dice overlap reported
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Dissertations / Theses on the topic "Tumors classification"

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Berger, Johanna. "Classification of Lung Tumors by using Deep Learning." Thesis, KTH, Optimeringslära och systemteori, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229729.

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The aim of this thesis is to apply deep learning on medical images in order to build an image recognition algorithm. The medical images used for this purpose are CT scans of lung tissue, which is a three dimensional image of a patients lungs. The purpose is to design an image recognition algorithm that is able to differentiate between tumors and normal tissue in lungs. The algorithm is based on artificial neural networks and therefore the ability of a convolutional neural network (CNN) to predict a tumor is studied. Two different architectures are designed in this thesis, which are a three and six layer CNN. In addition, different hyper-parameters and optimizers are compared in order to find suitable settings. This thesis concludes that no significant difference exists between the results of the two architectures. The architecture with three layers is faster to train and therefore 100 trainings with same settings are completed with this architecture in order to get statistics of the trainings. The mean accuracy for the test set is 91:1% and the standard-deviation for the test set is 2:39%. The mean sensitivity is 89:7% and the mean specificity is 92:4%.
Syftet med denna rapport är att utvärdera tillämpningen av djupinlärning på medicinska bilder för att konstruera en bildigenkänningsalgoritm. För detta ändamål används CT-bilder av lungvävnad, vilket är en avbildning av en patients lungor i tre dimensioner. Avsikten är att konstruera en bildigenkänningsalgoritm som är kapabel att differentiera mellan tumörer och normal vävnad i lungor. Algoritmen baseras på artificiella neuronnätverk och därför studeras ett faltande neuronnätverks förmåga att prediktera en tumör i lungvävnad. Vidare utvärderar denna rapport två olika arkitekturer – faltande neuronnätverk av tre respektive sex lager. Slutligen optimeras algoritmen med avseende på hyper-parametrar och optimeringsmetoder. Denna studie konkluderar att det inte råder någon signifikant skillnad mellan resultaten för de två arkitekturerna. Arkitekturen med tre lager går snabbare att träna och därför är 100 träningar med samma inställningar genomförda med denna arkitektur för att erhålla statistik över träningarna. Medelvärdet för noggrannheten för testdata är 91:1% med en standardavvikelse om 2.39 %. Medelvärdet för känsligheten är 89:7% och medelvärdet för specificiteten är 92:4%.
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Neto, Daniel Messias de Moraes. "Predição do risco individual de micrometástase do tumor carcinóide típico broncopulmonar em função de variáveis clínicas, anatomopatológicas e biomarcadores teciduais." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/5/5156/tde-06052011-115608/.

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Introdução: Os tumores carcinóides broncopulmonares típicos são proliferações malignas neuroendócrinas. Até bem pouco tempo eram consideradas como adenomas, isto é, tumores benignos. Porém com o avanço dos estudos anatomopatológicos, foi identificada a sua face maligna, pois apresenta as principais características das neoplasias malignas, quais sejam: metástase e invasão tecidual local. Além das metástases, estes tumores são capazes de produzir outra entidade ainda pouco estudada e conhecida que é a micrometástase. Estas correspondem a metástases menores que 2mm de diâmetro, que podem ou não se desenvolver, causando recidiva tumoral. Por sua vez as micrometástases são divididas em grupos de células tumorais, com diâmetro de 0,2 a 2mm e células tumorais isoladas, com diâmetro menor do que 0,2 mm. A literatura nos mostra que a incidência de micrometástase varia entre 10 a 90% dos pacientes em diversos tumores estudados. No caso dos carcinóides típicos temos pouca informação a respeito, sendo que a literatura nos mostra que a micrometástase em tumores carcinóides é considerada com fator de pior prognóstico. Porém não é o que observamos clinicamente, uma vez que temos o seguimento de inúmeros pacientes por mais de 10 anos, sem a recidiva tumoral em linfonodos mediastinais (seguimento clínicoradiológico). Objetivos: Verificar a presença de micrometástases em suas diversas formas, em pacientes portadores de carcinóide típico broncopulmonar, e verificar a possibilidade da predição do risco individual destas micrometástases em função de variáveis clínicas, anatomopatológicas e biomarcadores teciduais. Casuística e Métodos: Quarenta e nove pacientes portadores de carcinóide típico broncopulmonar com acompanhamento mínimo de 5 anos foram estudados. Todos foram submetidos a ressecção linfonodal por amostragem ou radical. As seguintes variáveis foram coletadas dos prontuários ou por entrevista: gênero, idade, localização do tumor em relação à carina (central ou periférico), diâmetro da lesão, comprometimento da margem cirúrgica, estadiamento TNM, ocorrência de metástases linfonodais, bem como quantidade de linfonodos acometidos por neoplasia em relação ao total dissecado, metástases à distância e tempo de sobrevivência. Os linfonodos foram analisados por coloração de hematoxilina-eosina e por imuno-histoquímica (Sinaptofisina e Cromogranina A) para pesquisa de micrometástase. Resultados: O grupo foi composto por 19 homens (38,8%) e 30 mulheres (61,2%). A idade média dos pacientes foi de 41,3 anos. Houve uma distribuição regular entre todos os lobos pulmonares acometidos. Em relação às vias aéreas, 78% dos tumores eram centrais e 22% eram periféricos. O diâmetro do maior eixo do tumor primário dos 49 pacientes variou de cinco a 80 milímetros, com mediana de 25 e intervalo interquartil 25 a 75% entre 16 e 35 milímetros. Em 54% dos casos foi realizada lobectomia pulmonar, 18% pneumonectomia, 12% bilobectomias e 16% procedimentos poupadores (segmentectomias, broncoplastias e nodulectomias). Em 12% dos casos houve comprometimento da margem cirúrgica. Em 42,8% dos casos houve imunomarcação por pelo menos um dos biomarcadores Sinaptofisina ou Cromogranina A para micrometástase. Em 18,4% dos casos foi diagnosticada macrometástase linfática, e em 1 caso ocorreu metástase hematogênica. Foram realizadas 4 baterias de testes avaliando os grupos sem e com metástases/micrometástases para se verificar a possibilidade de predição do risco individual de micrometástase. Conclusão: Foi possível encontrar micrometástases linfáticas utilizando imuno-histoquímica (Sinaptofisina e Cromogranina A). Não foi possível predizer o risco individual de micrometástases nos grupos estudados. Não houve diferença entre os grupos sem e com qualquer tipo de micrometástase. Não foi possível estabelecer correlação entre incidência de metástase e micrometástase nesta amostra populacional.
Introduction: The typical lung carcinoids are neuroendocrine tumors. Until short time ago they were considered adenomas, that is, benign tumors. Although, due to the anatomopathologic advances, it was identified its malignant behavior, once it presents the main characteristics of the malignant tumors: matastasis and local invasion. Beyond the metastasis, this tumor is able to produce another entity not yet well studied, the micrometastasis. This corresponds to metastasis shorter than 2mm in diameter that can or not develop and cause tumoral recurrence. The micrometastasis are divided in two groups: clusters, with diameter between 0,2 and 2mm, and isolated tumor cells, with diameter less than 0,2mm. The medical literature shows that the incidence of micrometastasis of different tumors has a wide variation, between 10 to 90%. In the case of the typical lung carcinoids few information is presented, and the presence of the micrometastasis worsen prognosis. On the other hand this is not what we usually see clinically, once the follow up of numerous patients of our casuistic for more than 10 years did not show the recurrence of the desease in the mediastinal lymphnodes. Objectives: Verify the presence of micrometastasis in its various forms in patients comited by lung carcinoid tumors and verify the possibility to predict the individual risk of micrometastasis from clinical and anatomopathological variables and tissue biomarkers. Casuistic and Methods: Forty nine patients with lung carcinoid tumors with follow up of at least 5 years were studied. All of them were submitted to mediastinal lymphnode dissection during the surgical procedure. The data collected was: age, gender, tumor location (central or peripherical), diameter, compromised surgical edge, TNM stage, lymphatic metastasis, hematogenic metastasis and survive. The lymphnodes were analised by Hematoxilin-Eosin and immunohistochemistry (Synaptophysin and Chromogranin A) in order to search for micrometastasis. Results: There were 19 men (38,8%) and 30 women (61,2%) with a mean age of 41,3 years. There was a regular distribution in all pulmonary lobes. There were 78% of central and 22% periferic tumors. The diameter varied between 0,5 to 80mm, with median in 25. In 54% of the cases was performed pulmonary lobectomy, in 18% pneumonectomy, in 12% bilobectomy and in 16% other procedures (bronchoplasty, wedge resection, nodulectomy). In 42,8% there was immunostaining with Synatophysin or Chromogranin A to micrometastasis. In 18,4% was diagnosed macrometastasis and in 1 case there was haematogenic metastasis. It was done 4 batteries of statistical tests to verify the possibility of prediction of the individual risk of micrometastasis. Conclusion: It was possible to find lymphatic micrometastasis using immunostaining with Synaptophysin and Chromogranin A. It was not possible to predict the individual risk of micrometastasis in the studied groups. There was no difference between the groups with or without micrometastasis. It was not possible to estabilish a correlation between the incidence of macro and micrometastasis in this population.
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Qureshi, Hammad A. "Meningioma classification using an adaptive discriminant wavelet packet transform." Thesis, University of Warwick, 2009. http://wrap.warwick.ac.uk/2790/.

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Meningioma subtypes classification is a real world problem from the domain of histological image analysis that requires new methods for its resolution. Computerised histopathology presents a whole new set of problems and introduces new challenges in image classification. High intra-class variation and low inter-class differences in textures is often an issue in histological image analysis problems such as Meningioma subtypes classification. In this thesis, we present an adaptive wavelets based technique that adapts to the variation in the texture of meningioma samples and provides high classification accuracy results. The technique provides a mechanism for attaining an image representation consisting of various spatial frequency resolutions that represent the image and are referred to as subbands. Each subband provides different information pertaining to the texture in the image sample. Our novel method, the Adaptive Discriminant Wavelet Packet Transform (ADWPT), provides a means for selecting the most useful subbands and hence, achieves feature selection. It also provides a mechanism for ranking features based upon the discrimination power of a subband. The more discriminant a subband, the better it is for classification. The results show that high classification accuracies are obtained by selecting subbands with high discrimination power. Moreover, subbands that are more stable i.e. have a higher probability of being selected provide better classification accuracies. Stability and discrimination power have been shown to have a direct relationship with classification accuracy. Hence, ADWPT acquires a subset of subbands that provide a highly discriminant and robust set of features for Meningioma subtype classification. Classification accuracies obtained are greater than 90% for most Meningioma subtypes. Consequently, ADWPT is a robust and adaptive technique which enables it to overcome the issue of high intra-class variation by statistically selecting the most useful subbands for meningioma subtype classification. It overcomes the issue of low inter-class variation by adapting to texture samples and extracting the subbands that are best for differentiating between the various meningioma subtype textures.
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Hrabovszki, Dávid. "Classification of brain tumors in weakly annotated histopathology images with deep learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177271.

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Brain and nervous system tumors were responsible for around 250,000 deaths in 2020 worldwide. Correctly identifying different tumors is very important, because treatment options largely depend on the diagnosis. This is an expert task, but recently machine learning, and especially deep learning models have shown huge potential in tumor classification problems, and can provide fast and reliable support for pathologists in the decision making process. This thesis investigates classification of two brain tumors, glioblastoma multiforme and lower grade glioma in high-resolution H&E-stained histology images using deep learning. The dataset is publicly available from TCGA, and 220 whole slide images were used in this study. Ground truth labels were only available on whole slide level, but due to their large size, they could not be processed by convolutional neural networks. Therefore, patches were extracted from the whole slide images in two sizes and fed into separate networks for training. Preprocessing steps ensured that irrelevant information about the background was excluded, and that the images were stain normalized. The patch-level predictions were then combined to slide level, and the classification performance was measured on a test set. Experiments were conducted about the usefulness of pre-trained CNN models and data augmentation techniques, and the best method was selected after statistical comparisons. Following the patch-level training, five slide aggregation approaches were studied, and compared to build a whole slide classifier model. Best performance was achieved when using small patches (336 x 336 pixels), pre-trained CNN model without frozen layers, and mirroring data augmentation. The majority voting slide aggregation method resulted in the best whole slide classifier with 91.7% test accuracy and 100% sensitivity. In many comparisons, however, statistical significance could not be shown because of the relatively small size of the test set.
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Baish, James W., and Rakesh Jain. "Diffusion in tumors and normal tissues." Diffsuion fundamentals 16 (2011) 4, S. 1, 2011. https://ul.qucosa.de/id/qucosa%3A13733.

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Silva, Fernando Moura. "Expressão de receptores de somatostatina subtipo 2 (SSTR-2) e a sua relação com metástase linfática e variáveis clínicas pré-operatórias em tumores carcinóides broncopulmonares típicos." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/5/5156/tde-19112008-165312/.

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Os tumores carcinóides broncopulmonares típicos (CT) são proliferações de células neuroendócrinas. Foram consideradas como adenomas e acreditava-se que não tinham potencial para disseminação hematogênica e linfática. Porém, a ocorrência de metástase linfática e hematogênica acontece em um quinto dos indivíduos acometidos por essa patologia. A variação no comportamento clínico dos carcinóides broncopulmonares torna imperativa a realização de pesquisas que visem à melhor compreensão dessa doença. É fundamental determinar a agressividade e o risco individual da ocorrência de metástase linfática e hematogênica para que se possa oferecer um tratamento individualizado para cada binômio doente-doença. A classificação atual divide os tumores carcinóides, conforme o grau histológico de malignidade em típico e atípico, agrupando as neoplasias de acordo com o índice mitótico, relação volumétrica núcleo/citoplasma, presença ou ausência de necrose, pleomorfismo nuclear e invasão vascular. Receptores celulares na superfície externa da membrana plasmática podem ser ubiquamente expressos em diversos tipos celulares ou específicos para determinada população celular. Os receptores de somatostatina são específicos de células neuroendócrinas e também são expressos nas neoplasias desta natureza. Existem 5 tipos de receptores de somatostatina (SSTR). A interação da somatostatina (SST) com seu receptor específico provoca as inibições do ciclo celular e da angiogêsese, bem como estimula a apoptose. A meia-vida plasmática da SST é breve. Análogos com menor metabolização eram necessários. Foram desenvolvidos os análogos como octreotide e lanreotide. Estes análogos foram acoplados à radionuclídeos, possibilitando aplicação em diagnóstico, estadiamento e tratamento dos tumores neuroendócrinos. O SSTR do tipo 2 possui maior afinidade pela somatostatina. A expressão imunohistoquímica de SSTR-2 em carcinóide típicos ofereceria métodos adicionais de diagnóstico e tratamento para esta doença. Com o objetivo de demonstrar a expressão de SSTR-2 em carcinóides broncopulmonares típicos, bem como verificar se existia relação entre a expressão e ocorrência de metástase linfática 62 pacientes tiveram suas amostras de tumor submetidas ao método imunohistoquímico. Verificou-se, ademais, se a expressão de SSTR-2 e o índice de expressão imunohistoquímica eram variáveis independentes preditivas do risco de metástase linfática. A relação entre expressão de SSTR-2 e variáveis clínicas pré-operatórias também foi analisada. 36 pacientes tinham tumores que expressavam SSTR-2 (58,1%), enquanto 26 doentes tinham tumores que não expressavam SSTR-2 (41,9%). Não existiu diferença estatística significante entre a expressão de SSTR-2 e a ocorrência de metástase linfática (teste exato de Fisher, p=0,529). Também não existiram diferenças estatísticas significantes nas análises multivariadas que testaram se tanto o SSTR-2 quanto o índice de imunohistoquímica eram variáveis independentes preditivas do risco de metástase linfática. Neste estudo, os CT expressaram SSTR-2. Além disso, não existiu relação entre a ocorrência de metástase linfática e a expressão de SSTR-2. Por fim, o SSTR-2 e o índice imunohistoquímico não foram variáveis independentes do risco de metástase linfática
Typical pulmonary carcinoids are neuroendocrine cells proliferations and they were former considered lung adenomas with no hematogenic or lymphatic metastatic potential. However, it is known that up to 20% of patients develop metastatic disease. It is mandatory that new studies be developed due to the variation in clinical presentation of these patients. It is also required that the individual risk of lymphatic and hematogenic metastasis be determined in order to individualize the patients treatment. Pulmonary carcinoids are classified according to hystologic grade. The current classification includes hystologic grade, presence or absence of necrosis, nuclear pleomorphism, and vascular invasion. Somatostatin receptors (SSTR) are neuroendocrine cell specific receptors and can be detected in neuroendocrine tumors as well. There are 5 SSTRs subtypes. Somatostatin is a peptide that inhibits the cell cycle and angiogenesis as well as increases the apoptosis by binding to SSTR. The use of long-acting form of octreotide (a SST analogue) has been associated with treatment (radiolabeled somatostatin analogs) and diagnosis (OctreoScan®). Encouraging results have been obtained with the use of radiolabeled somatostain analogs yttrium-90 and Lu-177 to treat patients with neuroendocrine tumors. This study was designed to evaluate if typical bronchopulmonary carcinoid expressed Somatostatin receptor type 2 using the the immunohistochemical technique to identify Somatostatin receptor type 2. This study verified if there was relation between Somatostatin receptor type 2 expression and lymphatic metastasis. Futhermore, we verified if Somatostatin receptor type 2 and imunnohistochemistry score would be independent preditive markers to lymphatic metastasis. 62 patients were evaluated. 36 (58,1%) patients expressed Somatostatin receptor type 2 in their tumor samples whereas 26 (41,9%) patients did not express Somatostatin receptor type 2. This study did not verify significant statistical difference between SSTR-2 expression and lymphatic metastasis. Somatostatin receptor type 2 and imunnohistochemistry score were not independent preditive markers to lymphatic metastasis. There were no significant statistical differences on multivariate analyses. In conclusion this study verified that there was Somatostatin receptor type 2 expression on tumor samples studied but there was no relation between Somatostatin receptor type 2 and lymphatic metastasis. Futhermore, Somatostatin receptor type 2 and its imunnohistochemistry score were not independent preditive markers to lymphatic metastasis
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Laloni, Mariana Tosello. "Predição do risco individual de metástase linfática e hematogênica em função da intensidade da linfangiogênese no tumor carcinóide típico broncopulmonar." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/5/5156/tde-14102008-154346/.

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Os tumores carcinóides típicos broncopulmonares são proliferações de células neuroendócrinas. Foram consideradas como adenomas e acreditava-se que não tinham potencial para disseminação hematogênica e linfática. Porém, a ocorrência de metástase linfática e hematogênica acontece em um quinto dos indivíduos acometidos por essa patologia. A variação no comportamento clínico dos carcinóides broncopulmonares torna imperativa a realização de pesquisas que visem à melhor compreensão dessa doença. É fundamental determinar a agressividade e o risco individual da ocorrência de metástase linfática e hematogênica para que se possa oferecer um tratamento individualizado para cada binômio doente-doença. A classificação atual divide os tumores carcinóides, conforme o grau histológico de malignidade em típico e atípico, agrupando as neoplasias de acordo com o índice mitótico, relação volumétrica núcleo/citoplasma, presença ou ausência de necrose, pleomorfismo nuclear e invasão vascular. Esta análise, porém, é realizada em espécimes histológicos corados pela hematoxilina-eosina, técnica tradicional consagrada, mas que não permite avaliar processos biomoleculares relacionados ao potencial maligno das células que já podem estar presentes e não serem detectados pelo método. Em tumores carcinóides vários estudos já foram realizados na tentativa de identificar o potencial proliferativo de células que ainda não apresentam figuras de mitose, como PCNA, p53, Ki-67, o processo apoptótico (Bcl-2, Bax e Bak), fibras do sistema colágeno e elástico e angiogênese. Entretanto, a linfangiogênese nunca foi estudada. Na última década várias moléculas funcionais e constitucionais que são expressas especificamente nas células do endotélio ou nos podócitos dos vasos linfáticos foram identificadas, como o VEGF-C, VEGFR-3 e o LYVE-1, possibilitando a melhor compreensão da linfangiogênese. Estudamos a imunomarcação dessas estruturas no carcinóide típico. Pela primeira vez no Brasil, a quantificação de vasos linfáticos foi realizada usando o LYVE1 como marcador. Apesar do uso de vários bloqueios de sítios inespecíficos não foi possível quantificar a expressão do VEGF-C e VEGFR-3 em carcinóides típicos, pois não encontramos controle interno negativo. Houve diferença significante entre as médias da idade em relação ao gênero. Não houve diferença significante entre as médias do diâmetro e número de linfonodos acometidos em relação ao gênero. Em relação ao grupo com e sem metástase encontramos difenca significante em relação ao diâmetro e ao comprometimento da margem. Não houve diferença da mediana do número de vasos linfáticos corados por mil células entre os grupos sem e com metástase linfática. Por regressão logística identificamos o diâmetro do tumor primário como uma variável independente preditiva do risco de metástase hematogênica e o diâmetro do tumor primário e a localização central ou periférica como variáveis independentes preditivas do risco de qualquer metástase (linfática ou hematogênica). O número de vasos linfáticos corados por mil células não foi identificado pelo modelo de regressão logistica como uma variável independente preditiva do risco individual de metástase linfática. Conclui-se que há correlação do diâmetro do tumor com o potencial de metástase hematogênica e há correlação entre diâmetro e localização do tumor primário e a ocorrência de metástase linfática ou hematogênica. A quantificação da imunoexpressão do LYVE-1 não demonstrou correlação. Outras técnicas devem ser estudadas e empregadas para identificar a importância da linfangiogênese no carcinóide típico.
Typical pulmonary carcinoids are neuroendocrine cells proliferations and they were former considered lung adenomas with no hematogenic or lymphatic metastatic potential. However, it is known that up to 20% of patients develop metastatic disease. It is mandatory that new studies be developed due to the variation in clinical presentation of these patients. It is also required that the individual risk of lymphatic and hematogenic metastasis be determined in order to individualize the patients treatment. Pulmonary carcinoids are classified according to hystologic grade. The current classification includes hystologic grade, presence or absence of necrosis, nuclear pleomorphism, and vascular invasion. This classification is based on Hematoxylin and Eosin stain and this technique can not assess biomolecular processes related to malignant potential. Trying to identify the malignant potential of the carcinoid tumors some studies have already been designed to identify some proteins as PCNA, p53, Ki-67, apoptosis proteins (Bcl-2, Bax and Bak), collagen and elastic fibers as well as angiogenic process. However, the lymphangiogenic mechanism has never been evaluated in typical pulmonary carcinoid tumors. Recently some molecules (VEGF-C, VEGFR-3 and LYVE-1) that are specifically expressed in the endothelium of the lymphatic vessels have been identified. These findings have improved the lymphangiogenic mechanism comprehension. This study used the immunohistochemical technique to identify VEGF-C, VEGFR-3 and LYVE-1 in 182 patients submitted to surgical procedures to treat Typical pulmonary carcinoid tumors. Lymphatic metastasis were diagnosed in 23 of 182 patients and 17 of 182 patients were identified with hematogenic metastasis. Futhermore, this study was the first reported one which has tried to quantify the lymphatic vessels using the LYVE-1 as an immunohistochemical marker. This study could not assess VEGF-C and VEGFR-e expression in Typical pulmonary carcinoids since an internal negative control could not be determined. There was a statistical difference between the median age and gender. There was no statistical difference between the median diameter and the number of positive lymph nodes related to the gender. This study demonstrated a statistical difference between the diameter and positive margins related to the group of patients that have developed metastatic disease and the group of patients with no metatastatic disease. There was no difference between the group of patients that have developed metastatic disease and the group of patients with no metatastatic disease according to the median number of lymphatic vessels stained. Based on logistic regression this study demonstrated that there is a predictive risk of developing hematogenic metastasis related to the diameter of the tumor. The predictive risk of the lymphatic metastasis was not improved by the number of the immunohistochemical stained lymphatic vessels, according to the logistic regression model. The immunohistochemical expression of LYVE-1 has not demonstrated statistical correlation between the parameters studied. Other than immnuhistochemical techniques are required to improve the comprehension of the lymphangiogenic mechanism involved in the Typical pulmonary carcinoid tumor
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Cuellar, Baena Sandra Patricia. "Quantificação de sinais de MRS do cérebro in-vivo para classificação de tumores." [s.n.], 2008. http://repositorio.unicamp.br/jspui/handle/REPOSIP/277860.

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Orientador: Gabriela Castellano
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Fisica Gleb Wataghin.
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Resumo: Este trabalho visou o estudo e validação de técnicas de pre-processamento e quantificação de dados provenientes da técnica de Espectroscopia por Ressonância Magnética (MRS, do inglês Magnetic Resonance Spectroscopy), obtidos do cérebro humano in vivo, para a extração de informação que fosse clinicamente relevante para o estudo e diagnostico de tumores cerebrais. Para isso, foi feito o estudo da técnica com base na literatura, incluindo a revisão dos aspectos físicos envolvidos, estudando os métodos computacionais utilizados para o pre-processamento e quantificação dos dados, e os aspectos bioquímicos dos metabólicos de interesse presentes no cérebro humano, passiveis de serem quantificados através da técnica. Especificamente, foi estudado um método de quantificação de dados de MRS, o método. AMARES (Advanced Method for Accurate, Robust and Efficient Spectral fitting of MRS data), aplicado na quantificação de dados de MRS adquiridos de sujeitos controles e pacientes portadores de tumores cerebrais, provenientes de uma base de dados do Laboratório de Neuroimagem (LNI - Hospital das Clinicas - UNICAMP). Isso foi feito utilizando o software de domínio público jMRUI (http://sermn02.uab.es/mrui/)[1], que possui o método AMARES já implementado. Estes resultados foram comparados com resultados provenientes de uma quantificação manual desses mesmos dados, realizada previamente como parte do projeto de doutorado da Dra. Andréia Vasconcellos (atual docente do Depto. de Radiologia da FCM/UNICAMP)[2]. Foi verificada a concordância entre os dois métodos de quantificação, e também a viabilidade de usar os resultados da quantificação com o método automático para alem de diferenciar entre os grupos de pacientes e controles, realizar a separação dos Pacientes com tumores em diferentes grupos. Obteve-se que os resultados obtidos com o método automático foram mais precisos e consistentes que os obtidos com o método manual, e permitiram uma melhor classificação dos tipos de tumores. Adicionalmente, foram incluídos neste trabalho os resultados do estudo de perfis metabólicos ex vivo em tumores cerebrais pediátricos através da técnica HR-MAS (do inglês High Resolution Magic Angle Spinning). Este estudo adicional foi realizado no Laboratório de Imagem Molecular da Faculdade de Medicina da Universidade de Valencia (Espanha) através do Programa Santander de Mobilidade Internacional e financiado através de uma bolsa do Banco Santander-Banespa.
Abstract: The aim of this work was to study and validate techniques for pre-processing and quantificating Magnetic Resonance Spectroscopy data, obtained in vivo from the human brain, in order to get information clinically useful for the study and diagnosis of brain tumors. Therefore, a literature-based study of the technique was made, including a review of the Physics concepts involved, the data acquisition process in the scanner and the computational methods used to pre-process and quantificate the spectral data, as well as the biochemical aspects of the metabolites of interest in the human brain that can be detected by this technique. Special attention was given to the AMARES (Advanced Method for Accurate, Robust and Efficient Spectral fitting of MRS data) method for MRS data quantification, which was studied and applied to the quantification of data from control subjects and patients with brain tumors. The data came from a database of the Neuroimaging Laboratory (LNI - Hospital das Clinicas - UNICAMP). The quantification with AMARES was made through the jMRUI software (http://sermn02.uab.es/mrui/) [1], a public domain software for processing and quantification of MRS data. These results were compared to the results obtained with a manual quantification of the same data, previously done as part of the PhD thesis work of Dr. Andreia Vasconcellos (lecturer from the Radiology Department of the School of Medicine, UNICAMP) [2]. The agreement between the results from both quantification methods was verified, as well as the feasibility of using the automatic quantification results to differentiate among tumor types, besides differentiating between patients and controls. Results obtained by the automatic method were more accurate and consistent than those obtained by the manual method allowing a better classification. Additionally, in this work were included the results of the study of ex vivo and in vivo metabolic profiling in pediatric brain tumors using the HR-MAS (High Resolution Magic Angle Spinning) technique. This study was carried out in the Molecular Imaging Laboratory, School of Medicine at the University of Val¿encia (Spain), within the Santander-Banespa Bank International Exchange Program.
Mestrado
Física
Mestre em Física
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Stenman, Jakob. "mRNA-expression-based classification of solid tumors : development of accurate amplification-based quantification techniques." Helsinki : University of Helsinki, 2002. http://ethesis.helsinki.fi/julkaisut/laa/kliin/vk/stenman/.

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Bari, Muhammad Furqan. "Biomarkers for the classification of high grade neuroendocrine lung cancers." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/56420/.

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In the management of lung cancer the most important step beyond establishing the presence a malignant tumour is identifying whether the tumour is small cell lung cancer (SCLC) or one of the variants of non-small cell carcinoma (NSCLC), which includes adenocarcinoma (AD), squamous (SCC), large cell carcinoma (LCC), large cell neuroendocrine carcinoma (LCNEC), typical carcinoids (TC) and atypical carcinoids (AT). SCLC is a high grade neuroendocrine tumour which usually presents as central mass. These tumours are not usually amenable to curative surgical resection and are treated primarily by chemotherapy resulting in a treatment dichotomy of SCLC and NSCLC. The diagnosis of the tumour subtypes is routinely established on cytology or histology samples and in case of AD, LCC and SCC, which are not neuroendocrine tumours, the diagnosis is aided by neuroendocrine markers. However for TC, AT and LCNEC which are neuroendocrine tumours, the diagnosis is based on morphological features alone, which in some cases overlap and result in difficulty in diagnosis. This inter-observer variability is common among the neuroendocrine lung tumours and is highest between SCLC and LCNEC followed by TC and AC. Currently no marker or ancillary stain are clinically in practice which can aid in classification of these neuroendocrine tumours. In an attempt to address this issue, this project evaluated the use of bioinformatics to analyze publicly available high through-put transcriptomic data to identify markers which would aid in the distinction of SCLC from LCNEC. However, the markers identified were found to have low specificity and sensitivity, leading to the conduction of a de novo gene expression study utilizing laser micro-dissection of pure tumour samples of SCLC and LCNEC. This experiment yielded a different set of top ranked discriminator genes of which validation at the protein level by immunocytochemistry supported CDX2, CD99 and CD44 as LCNEC specific markers and BAI3 as a SCLC specific marker.
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Books on the topic "Tumors classification"

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H, Sobin L., Wittekind Ch, and International Union against Cancer, eds. TNM classification of malignant tumours. 6th ed. New York: Wiley-Liss, 2002.

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Ch, Wittekind, ed. TNM atlas: Illustrated guide to the TNM classification of malignant tumours. Hoboken, N.J: Wiley, 2005.

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H, Sobin L., Wittekind Ch, and International Union against Cancer, eds. TNM classification of malignant tumours. 5th ed. New York: J. Wiley, 1997.

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Paul, Hermanek, Sobin L. H, and International Union against Cancer, eds. TNM classification of malignant tumours. 4th ed. Berlin: Springer-Verlag, 1992.

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Cancer, International Union against, ed. TNM classification of malignant tumours. 7th ed. Chichester, West Sussex, UK: Wiley-Blackwell, 2010.

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Mohr, Ulrich, ed. International Classification of Rodent Tumors. The Mouse. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-07973-7.

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Kokusai shippei bunrui, shuyōgaku: ICD-O : International classification of diseases for oncology. 3rd ed. [Tokyo]: Kōsei Rōdōshō Daijin Kanbō Tōkei Jōhōbu, 2002.

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Fritz, April. Clasificación Internacional de Enfermedades para Oncología (CIE-O) 3a. ed. Washington: Pan American Health Organization, 2003.

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U, Mohr, and International Agency for Research on Cancer., eds. International classification of rodent tumours. Lyon, France: International Agency for Research on Cancer, 1992.

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TNM atlas. Chichester, West Sussex, UK: Wiley Blackwell, 2014.

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Book chapters on the topic "Tumors classification"

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Zülch, Klaus J. "Classification of Brain Tumors." In Brain Tumors, 1–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-68178-3_1.

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Biswas, Arnab. "Classification of Eyelid Tumors." In Eyelid Tumors, 29–31. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1874-6_4.

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Chawla, Bhavna. "Retinoblastoma: Diagnosis, Classification and Management." In Intraocular Tumors, 1–18. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0395-5_1.

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Veksler, Ronel, and Ido Didi Fabian. "Uveal Melanoma: Diagnosis, Classification and Management." In Intraocular Tumors, 71–80. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0395-5_6.

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Schiffer, Davide, Maria Teresa Giordana, Alessandro Mauro, and Riccardo Soffietti. "Classification and Nosography of Neuroepithelial Tumors." In Brain Tumors, 96–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60529-1_6.

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Damato, Bertil E., and Sarah E. Coupland. "Classification of Uveal Tumors." In Clinical Ophthalmic Oncology, 11–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17879-6_2.

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Watanabe, Shaw. "Histiocytic Tumors: Immunologic Classification." In Local Invasion and Spread of Cancer, 162–71. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-1093-5_13.

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Damato, Bertil, and Sarah E. Coupland. "Classification of Uveal Tumors." In Clinical Ophthalmic Oncology, 11–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54255-8_2.

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Claussen, Claus, Rudolf Fahlbusch, Roland Felix, Thomas Grumme, Jürgen Heinzerling, José R. Iglesias-Rozas, Ekkehard Kazner, et al. "Classification of Brain Tumors." In Computed Tomography and Magnetic Resonance Tomography of Intracranial Tumors, 2–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74311-5_2.

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Cecconi, Lucia, Alfredo Pompili, Fabrizio Caroli, and Ettore Squillaci. "Classification of CNS tumors." In MRI Atlas of Central Nervous System Tumors, 85–94. Vienna: Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-9178-1_3.

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Conference papers on the topic "Tumors classification"

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Anparasy, S. "Classification of Breast cancer tumors using Feature Selection and CNN." In ERU Symposium 2021. Engineering Research Unit (ERU), University of Moratuwa, 2021. http://dx.doi.org/10.31705/eru.2021.11.

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Breast cancer is one of the most dangerous diseases in the world and almost two million new cases are diagnosed every year. It starts from the breasts tissue and then spreads to other parts of the body. Early detection of breast cancer is important to save the life of a woman as it is related with a risen number of available treatment options. Benign and malignant are the major types of tumors and they are cancerous and non-cancerous, respectively. Benign is not dangerous since it does not destroy the nearby tissues and cannot spread or grow. Malignant tumor invades neighbouring tissues, blood vessels and spreads to other parts of the body by metastasis. Therefore, differentiating malignant from benign will help to detect breast cancer in its early stage. Nowadays, machine learning techniques are used to classify the tumor types hence the quality of lift is increased.
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Kumar, Vinod, Jainy Sachdeva, Indra Gupta, Niranjan Khandelwal, and Chirag Kamal Ahuja. "Classification of brain tumors using PCA-ANN." In 2011 World Congress on Information and Communication Technologies (WICT). IEEE, 2011. http://dx.doi.org/10.1109/wict.2011.6141398.

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McIntosh, Laura M., James R. Mansfield, A. Neil Crowson, John W. P. Toole, Henry H. Mantsch, and Michael Jackson. "Classification of infrared spectra from skin tumors." In BiOS 2000 The International Symposium on Biomedical Optics, edited by R. Rox Anderson, Kenneth E. Bartels, Lawrence S. Bass, C. Gaelyn Garrett, Kenton W. Gregory, Nikiforos Kollias, Harvey Lui, et al. SPIE, 2000. http://dx.doi.org/10.1117/12.386244.

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Brodsky, Alexander, Kevin S. Guo, Amanda Khoo, Vahid Agbortoko, Dongfang Yang, Elizabeth Y. Wu, and Ian Y. Wong. "Abstract PO019: Classification of tumors by collagen expression reveals genotype-tumor ECM interactions." In Abstracts: AACR Virtual Special Conference: The Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; January 11-12, 2021. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.tme21-po019.

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Chatterjee, Subarna, Ajoy Kumar Ray, Rezaul Karim, and Arindam Biswas. "Classification of malignant tumors using multiple sonographic features." In 2011 International Conference on Recent Trends in Information Systems (ReTIS). IEEE, 2011. http://dx.doi.org/10.1109/retis.2011.6146877.

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DAGLI, Kaya, and Osman EROGUL. "Classification of Brain Tumors via Deep Learning Models." In 2020 Medical Technologies Congress (TIPTEKNO). IEEE, 2020. http://dx.doi.org/10.1109/tiptekno50054.2020.9299231.

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Souto, Lizianne P. Marques, Thiago K. L. Dos Santos, and Marcelino Pereira S. Silva. "Classification of Breast Tumors Through Image Mining Techniques." In XVIII Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbcas.2018.3667.

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Zhu, Zhuotun, Yongyi Lu, Wei Shen, Elliot K. Fishman, and Alan L. Yuille. "Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors." In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00379.

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Mohamed, Sellam, Abdallah Marhraoui Hsaini, Idriss Chana, Aziz Bouazi, and Roukhe Ahmed. "Two Parallel CNN Blocks for brain tumors classification." In 2022 IEEE 3rd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS). IEEE, 2022. http://dx.doi.org/10.1109/icecocs55148.2022.9983133.

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Da Silva, Valesca J. S., Mateus M. R. Da Silva, Marcelino P. S. Silva, and Joana R. C. Nogueira. "BI-RADS Breast Tumor Classification Through Image Mining." In VII Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/kdmile.2019.8791.

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In this article, a computer aided diagnostic system for BI-RADS classification of breast cancer is proposed. The approach involves image processing capabilities to extract features from tumors in mammography and image mining to classify them as BI-RADS 2, BI-RADS 3, BI-RADS 4C or BI-RADS 5. Images from the BCDR repository were used for the experiments. The results showed the efficacy of the proposed method, which classified tumors with considerable accuracy in four BI-RADS categories.
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Reports on the topic "Tumors classification"

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Adamczyk, Lukasz, and Jon Oxley. Pathological classification of renal cell tumours. BJUI Knowledge, January 2016. http://dx.doi.org/10.18591/bjuik.0098.

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Adamczyk, Lukasz, Paidamwoyo Gwiti, and Jon Oxley. Pathological classification of renal cell tumours. BJUI Knowledge, May 2020. http://dx.doi.org/10.18591/bjuik.0098.v2.

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Adamczyk, Lukasz, and Jon Oxley. Pathological classification of non-RCC renal parenchymal tumours. BJUI Knowledge, January 2016. http://dx.doi.org/10.18591/bjuik.0634.

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Adamczyk, Lukasz, Paidamwoyo Gwiti, and Jon Oxley. Pathological classification of non-RCC renal parenchymal tumours. BJUI Knowledge, May 2020. http://dx.doi.org/10.18591/bjuik.0634.v2.

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Hedyehzadeh, Mohammadreza, Shadi Yoosefian, Dezfuli Nezhad, and Naser Safdarian. Evaluation of Conventional Machine Learning Methods for Brain Tumour Type Classification. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, June 2020. http://dx.doi.org/10.7546/crabs.2020.06.14.

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Tian, Cong, Jianlong Shu, Wenhui Shao, Zhengxin Zhou, Huayang Guo, and Jingang Wang. The efficacy and safety of IL Inhibitors, TNF-α Inhibitors, and JAK Inhibitor on ankylosing spondylitis: A Bayesian network meta-analysis of a “randomized, double-blind, placebo-controlled” trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0117.

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Review question / Objective: In this study, we conducted a Bayesian network meta-analysis to evaluate the efficacy and safety of interleukin (IL) inhibitors, tumor necrosis factor-alpha (TNF-α) inhibitors, and Janus kinase (JAK) inhibitors on ankylosing spondylitis (AS).The purpose of this study is to compare the effectiveness and safety of different interventions for treating AS to provide insights into the decision-making in clinicalpractice. Condition being studied: Ankylosing spondylitis. Based on the Bayesian hierarchical model, we conducted a network meta-analysis using the gemtc package in R software (version 4.1.3) and Stata software (version 15.1). Cong Tian and Jianlong Shu contributed to the conception and design of the study and supervised the tweet classification. All authors drafted the manuscript. Wenhui Shao, Zhengxin Zhou, Huayang Guo and Jingang Wang contributed to data management and tweet classification. Cong Tian, Jianlong Shu and Zhengxin Zhou performed the statistical analysis. Cong Tian, Jianlong Shu, Wenhui Shao and Zhengxin Zhou reviewed the manuscript.
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Anantharajan, Shenbagarajan, and Shenbagalakshmi Gunasekaran. Detection and Classification of MRI Brain Tumour Using GLCM and Enhanced K-NN. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2021. http://dx.doi.org/10.7546/crabs.2021.02.13.

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Arun, Ramaiah, and Shanmugasundaram Singaravelan. Classification of Brain Tumour in Magnetic Resonance Images Using Hybrid Kernel Based Support Vector Machine. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, October 2019. http://dx.doi.org/10.7546/crabs.2019.10.12.

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