Journal articles: 'Brain tumour'

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Watson, Shaun. "Brain tumour." Lancet 359, no. 9301 (January 2002): 177. http://dx.doi.org/10.1016/s0140-6736(02)07388-9.

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Srivastava, Smriti. "Brain Tumor Prediction Using Neural Network." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1513–17. http://dx.doi.org/10.22214/ijraset.2021.36616.

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Brain Tumor is a disease in which there is an abnormal growth of mass that occurs inside human brain that can led to death also. The detection of brain tumor takes places through MRI scan images. For doctors sometimes it becomes difficult to differentiate between tumour cells and nerve cells. Even sometimes what happens is that unstructured shape of tumours led it make difficult for doctors to identify tumours in brain. Artificial intelligence is one of the most trending technologies now a day through which machines gets the power to think and take decisions on its own. This paper uses the power of Artificial Intelligence to detect Brain tumour in human brain.

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Malarvizhi, A. B., A. Mofika, M. Monapreetha, and A. M. Arunnagiri. "Brain tumour classification using machine learning algorithm." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012042. http://dx.doi.org/10.1088/1742-6596/2318/1/012042.

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Abstract A Brain tumour is formed by a gradual addition of abnormal cells, and this is one of the major causes of death among other sorts of cancers. It is necessary to classify brain tumor using Magnetic Resonance Imaging (MRI) brain tumor image for treatment because MRI images assist as to detect the smallest defect of the body. This paper aimed to automatically classify brain tumours using a machine learning algorithm. In this work, the input image of the brain was pre-processed using median filter, segmented from the background using thresholding and K-means clustering algorithm and its features were extracted using GLCM. Using the SVM classifier, the brain tumour in the image was detected as either benign or malignant. This image classification process helps the doctors and research scientists to detect the tumour during its early stages, thereby controlling the spread of cancerous cells.

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Dirks, Peter B. "Brain tumour stem cells: the undercurrents of human brain cancer and their relationship to neural stem cells." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1489 (February 19, 2007): 139–52. http://dx.doi.org/10.1098/rstb.2006.2017.

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Conceptual and technical advances in neural stem cell biology are being applied to the study of human brain tumours. These studies suggest that human brain tumours are organized as a hierarchy and are maintained by a small number of tumour cells that have stem cell properties. Most of the bulk population of human brain tumours comprise cells that have lost the ability to initiate and maintain tumour growth. Although the cell of origin for human brain tumours is uncertain, recent evidence points towards the brain's known proliferative zones. The identification of brain tumour stem cells has important implications for understanding brain tumour biology and these cells may be critical cellular targets for curative therapy.

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Ahlbom, Anders, Ylva Rodvall, Y. Ben-Shlomo, and G. Davey Smith. "BRAIN TUMOUR TRENDS." Lancet 334, no. 8674 (November 1989): 1272–73. http://dx.doi.org/10.1016/s0140-6736(89)91873-4.

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Shivdikar, Adish, Mihir Shirke, Ishwar Vodnala, and Prof Jaychand Upadhaya. "Brain Tumor Detection using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 621–27. http://dx.doi.org/10.22214/ijraset.2022.40710.

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Abstract: Tumors are now the second major cause of cancer. A huge percentage of patients are in danger as more than just a consequence of cancer. The medical field needs fast, automated, efficient and reliable technique to detect tumor like brain tumor. Detection plays very important role in treatment. If proper detection of tumor is possible then doctors keep a patient out of danger. Various image processing techniques are used in this application. Doctors are able to provide excellent treatment and save a huge number of tumour patients by using this application. A tumour is nothing more than an uncontrolled growth of cells. Brain tumour cells expand to the point where they consume all of the nutrition intended for healthy cells and tissues, resulting in brain failure. Currently, doctors manually locate the position and area of a brain tumour by looking at the patient's MR images of the brain. This leads to inaccuracy in tumour detection and is extremely time intensive. A tumour is an uncontrollably growing clump of tissue. We can utilise CNN (Convolution Neural Network), also known as NN (Neural Network), and VGG 16 Deep Learning architectures (visual geometry group). To diagnose a brain tumour, transfer learning is used. The model's performance predicts whether or not a tumour is present in an image. If a tumour is present, the answer is yes; otherwise, the answer is no. Keywords: Brain Tumor, MRI, OpenCV, Data Augmentation, CNN, Transfer learning, VGG.

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S, Manimurugan. "Classification of Alzheimer's disease from MRI Images using CNN based Pre-trained VGG-19 Model." Journal of Computational Science and Intelligent Technologies 1, no. 2 (2020): 34–41. http://dx.doi.org/10.53409/mnaa.jcsit20201205.

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Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vectormachine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.

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T, Anitha, Charlyn Pushpa Latha G, and Surendra Prasad M. "A Proficient Adaptive K-means based Brain Tumor Segmentation and Detection Using Deep Learning Scheme with PSO." Journal of Computational Science and Intelligent Technologies 1, no. 3 (2020): 9–14. http://dx.doi.org/10.53409/mnaa.jcsit20201302.

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Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vector machine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.

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Nonaka, Motohiro, Misa Suzuki-Anekoji, Jun Nakayama, Hideaki Mabashi-Asazuma, Donald L. Jarvis, Jiunn-Chern Yeh, Kazuhiko Yamasaki, et al. "Overcoming the blood–brain barrier by Annexin A1-binding peptide to target brain tumours." British Journal of Cancer 123, no. 11 (September 14, 2020): 1633–43. http://dx.doi.org/10.1038/s41416-020-01066-2.

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Abstract Background Annexin A1 is expressed specifically on the tumour vasculature surface. Intravenously injected IF7 targets tumour vasculature via annexin A1. We tested the hypothesis that IF7 overcomes the blood–brain barrier and that the intravenously injected IF7C(RR)-SN38 eradicates brain tumours in the mouse. Methods (1) A dual-tumour model was generated by inoculating luciferase-expressing melanoma B16 cell line, B16-Luc, into the brain and under the skin of syngeneic C57BL/6 mice. IF7C(RR)-SN38 was injected intravenously daily at 7.0 μmoles/kg and growth of tumours was assessed by chemiluminescence using an IVIS imager. A similar dual-tumour model was generated with the C6-Luc line in immunocompromised SCID mice. (2) IF7C(RR)-SN38 formulated with 10% Solutol HS15 was injected intravenously daily at 2.5 μmoles/kg into two brain tumour mouse models: B16-Luc cells in C57BL/6 mice, and C6-Luc cells in nude mice. Results (1) Daily IF7C(RR)-SN38 injection suppressed tumour growth regardless of cell lines or mouse strains. (2) Daily injection of Solutol-formulated IF7C(RR)-SN38 led into complete disappearance of B16-Luc brain tumour in C57BL/6 mice, whereas this did not occur in C6-Luc in nude mice. Conclusions IF7C(RR)-SN38 crosses the blood–brain barrier and suppresses growth of brain tumours in mouse models. Solutol HS15-formulated IF7C(RR)-SN38 may have promoted an antitumour immune response.

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Muller, Paul J., and Brian C. Wilson. "Photodynamic Therapy of Malignant Brain Tumours." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 17, no. 2 (May 1990): 193–98. http://dx.doi.org/10.1017/s0317167100030444.

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ABSTRACT:Fifty patients with malignant supratentorial tumours were treated with intra-operative photodynamic therapy (PDT); in 33 cases the tumour was recurrent. In 45 patients the tumour was a cerebral glioma and in 5 cases a solitary cerebral metastasis. All patients received a porphyrin photosensitizer 18-24 hours pre-operatively. Photoillumination was carried out at 630 nm to a tumour cavity created by radical tumour resection and/or tumour cyst drainage. The light energy density ranged from 8 to 175 J/cm2. In 8 patients additional interstitial light was administered. The operative mortality was 4%. Follow up has ranged from 1 to 30 months. The median survival for the 45 primary malignant tumours was 8.6 months with a 1 and 2 year actuarial survival rate of 32% and 18%, respectively. In 12 patients a complete or near complete CT scan response was identified post PDT. These patients tended to have a tumour geometry (eg. cystic) that allowed complete or near complete light distribution to the tumour. The median survival for this group was 17.1 months with a 1 and 2 year actuarial survival of 62% and 38%, respectively. In the 33 cases who did not have a complete response the median survival was 6.5 months with a 1 and 2 year actuarial survival of 22% and 11%, respectively. Photodynamic therapy of malignant brain tumours can be carried out with acceptable risk. Good responses appear to be related to adequate light delivery to the tumour.

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Syed Ather Enam, Mashal Murad Shah, Mohammad Hamza Bajwa, Muhammad Usman Khalid, Saqib Kamran Bakhshi, Erum Baig, Iqbal Azam Altaf, et al. "The Pakistan Brain Tumour Epidemiology Study." Journal of the Pakistan Medical Association 72, no. 11 (December 15, 2022): S4—S11. http://dx.doi.org/10.47391/jpma.11-s4-akub02.

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Objective: To provide information about brain tumour epidemiology in Pakistan and potential associated risk factors due to family, medical and social characteristics. Methods: A retrospective cross-sectional nationwide study was designed by the Pakistan Society of Neuro-oncology, to include patients diagnosed with brain tumours in Pakistan retrospectively, from January 1, 2019- December 31, 2019. The study intended to involve data from all age groups for all brain tumour cases, irrespective of histopathology which would determine the national prevalence and incidence of these tumours. Results: A total of 2750 brain tumour cases were recorded, of which 1897 (69%) were diagnosed in the public sector. MRIs were a more common radiological study compared to CT scans. Gliomas were the most common tumours 778 (28.29%), while pineal tumours were the least common 19 (0.69%). The median age at diagnosis for males was 36 (24-49), while the median age at diagnosis for females was 37 (24-48). Hypertension was the most common co-morbidity in patients diagnosed with a brain tumour, 524 (51.89%), and smoking was the most frequent social behaviour, 355 (62.02%). Findings indicate a low metastasis frequency and few females seeking care. Conclusion: The PBTES and the PBTC have presented an opportunity and platform for hospitals and health professionals to work together to strengthen cancer care health systems, ensure implementation of treatment guidelines and conduct regular cancer registration. Keywords: brain neoplasms, registries, retrospective studies, epidemiology, Neuro-oncological surgery. Continue...

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Gaonkar, Omkar Maruti, Nitesh Sandip Jadhav, Dishant Krishna Koli, and Prof Vijay Bhosale. "Brain Tumour Diagnosis Using Matlab with Edge Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 122–28. http://dx.doi.org/10.22214/ijraset.2022.42105.

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Abstract: The segmenting brain tumours in magnetic resonance images (MRI) is a difficult task due to the variety of possible curves, spots, and image concentrations. Brain tumour segmentation is one of the most critical and challenging projects in the field of medical image processing because human-assisted manual characterization can result in inaccurate prediction and diagnosis. [1] A brain tumour is an unusual mass of tissue in which some cells multiply and grow uncontrollably. Furthermore, it is a difficult task when there is an enormous amount of information to be processed. Because brain tumours have a wide range of manifestations and coexist with normal tissues, extracting tumour regions from images becomes complicated. [2] Medical image processing provides basic information of abnormality of brain and it helps the doctor for best treatment planning. This paper specifically aims to detect and localisation tumour regions in the brain using the proposed methodology and patient MRI images.[3] We can derive detailed anatomical information from these high-resolution images in order to examine human brain development and detect abnormalities. Pre-processing, edge detection, and segmentation are the three stages of the proposed methodology. [4] Several tests are performed on the patient to detect cancer. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the most commonly used tests for locating brain tumours. The pre-processing stage involves the conversion of the original image to grayscale and removing any noise that has crept in. [5]The primary step in removing noise and smoothing an MRI image is pre-processing. Following that, segmentation is used to actually indicate the tumor-affected region in the MRI images. Finally, the watershed algorithm is being used to cluster the image. For the implementation of this system, we used MATLAB. Magnetic Resonance Imaging (MRI) has increased in popularity as a high-quality medical imaging technique. [6] The experimental results demonstrated that the proposed approach outperformed existing available approaches in terms of accuracy while maintaining the pathology experts' acceptable accuracy rate. Magnetic resonance imaging (MRI) is a specialized diagnostic imaging technique that provides comprehensive information about human soft tissue anatomy. This methodology allows for extensive clinical practice in the detection of brain tumours, making it simple to identify patients predicated on MR image data. In this paper, we propose a MATLAB programming technique for separating tumour images from brain magnetic resonance (MR) data.[7] The goal of segmentation is to simplify and/or change an image's representation into something more meaningful and easier to analyse. The accuracy of tumour detection is highly noticeable in the MRI image data, and the tumour is clearly highlighted using the proposed MATLAB Coding. These codes are used to enhance the MR image quality by trying to adjust the grey level and applying additional special filters. The MRI dataset confirms that the algorithm's outcomes are more applicable to ordinary output images to identify brain tumours.

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Kothari, Sonali, Shwetambari Chiwhane, Shruti Jain, and Malti Baghel. "Cancerous brain tumor detection using hybrid deep learning framework." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (June 1, 2022): 1651. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1651-1661.

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Computational <span>models based on deep learning (DL) algorithms have multiple processing layers representing data at multiple levels of abstraction. Deep learning has exploded in popularity in recent years, particularly in medical image processing, medical image analysis, and bioinformatics. As a result, deep learning has effectively modified and strengthened the means of identification, prediction, and diagnosis in several healthcare fields, including pathology, brain tumours, lung cancer, the abdomen, cardiac, and retina. In general, brain tumours are among the most common and aggressive malignant tumour diseases, with a limited life span if diagnosed at a higher grade. After identifying the tumour, brain tumour grading is a crucial step in evaluating a successful treatment strategy. This research aims to propose a cancerous brain tumor detection and classification using deep learning. In this paper, numerous soft computing techniques and a deep learning model to summarise the pathophysiology of brain cancer, imaging modalities for brain cancer, and automated computer-assisted methods for brain cancer characterization is used. In the sense of machine learning and the deep learning model, paper has highlighted the association between brain cancer and other brain disorders such as epilepsy, stroke, Alzheimer's, Parkinson's, and Wilson's disease, leukoaraiosis, and other neurological disorders.</span>

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Kieliszek, Agata, Blessing Bassey-Archibong, Chitra Venugopal, and Sheila K. Singh. "STEM-02. THERAPEUTIC INTERVENTION OF LUNG-, BREAST-, AND MELANOMA-BRAIN METASTASIS." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi21. http://dx.doi.org/10.1093/neuonc/noab196.078.

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Abstract BACKGROUND The incidence of brain metastases (BM) is tenfold higher than that of primary brain tumours. BM predominantly originate from primary lung, breast, and melanoma tumours with a 90% mortality rate within one year of diagnosis, posing a large unmet clinical need to identify novel therapies against BM.This unmet clinical need is largely attributed to a small population of cancer stem cells (CSCs), termed BM-initiating cells (BMICs), that are able to escape a primary tumour, drive metastasis and facilitate the formation of a secondary tumour in the brain. METHODS Using a large in-house biobank of patient-derived BMIC lines, the Singh Lab has generated murine orthotopic patient-derived xenograft models of BM and captured a “premetastatic” population of BMICs that have just seeded the brains of mice before forming clinically detectable tumours: a cell population that is impossible to detect in human patients but represents a therapeutic window wherein metastasizing cells can be targeted and eradicated before establishing clinically detectable tumours. RESULTS RNA sequencing of pre-metastatic BMICs from all three primary tumour models with subsequent Connectivity Map analysis identified a lead compound that exhibits selective anti-BM activity in vitro. Preliminary in vivo work has shown that this lead compound reduces the tumor burden of treated mice compared to vehicle control while providing a significant survival advantage. Ongoing mechanistic investigations aim to delineate the protein target of this compound in the context of the observed selective anti-BMIC phenotype. CONCLUSION Identification of novel small molecules that target premetastatic BM cells could prevent the formation of BM and dramatically improve the prognosis of at-risk cancer patients.

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Kumar, Vikas. "Segmentation of Brain Images by Optimizing Clustering of Convolution Based Features." E3S Web of Conferences 229 (2021): 01034. http://dx.doi.org/10.1051/e3sconf/202122901034.

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Brain tumour segmentation aims to separate the various types of tumour tissues like active cells, necrotic core, and edema from normal brain tissues of substantia alba (WM), grey matter (GM), and spinal fluid (CSF). Magnetic Resonance Imaging based brain tumour segmentation studies are attracting more and more attention in recent years thanks to non-invasive imaging and good soft tissue contrast of resonance Imaging (MRI) images. With the event of just about two decades, the ingenious approaches applying computer-aided techniques for segmenting brain tumour are getting more and more mature and coming closer to routine clinical applications. the aim of this paper is to supply a comprehensive overview for MRIbased brain tumour segmentation methods. Firstly, a quick introduction to brain tumours and imaging modalities of brain tumours is given in this proposed research, convolution based optimization. These stepwise step refine the segmentation and improve the classification parameter with the assistance of particle swarmoptimization.

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Vescovi, Angelo L., Rossella Galli, and Brent A. Reynolds. "Brain tumour stem cells." Nature Reviews Cancer 6, no. 6 (June 2006): 425–36. http://dx.doi.org/10.1038/nrc1889.

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Garfield, J. "Biology of Brain Tumour." Journal of Neurology, Neurosurgery & Psychiatry 50, no. 7 (July 1, 1987): 956. http://dx.doi.org/10.1136/jnnp.50.7.956.

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BOHNEN, NICHOLAS I. "Pesticides and Brain Tumour." International Journal of Epidemiology 23, no. 4 (1994): 867. http://dx.doi.org/10.1093/ije/23.4.867.

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Nilsson, Markus, Elisabet Englund, Filip Szczepankiewicz, Danielle van Westen, and Pia C. Sundgren. "Imaging brain tumour microstructure." NeuroImage 182 (November 2018): 232–50. http://dx.doi.org/10.1016/j.neuroimage.2018.04.075.

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Chaudhry, U. R., M. Farooq, F. Rauf, and S. K. Bhatti. "Tuberculosis Simulating Brain Tumour." Neuroradiology Journal 24, no. 3 (June 2011): 350–56. http://dx.doi.org/10.1177/197140091102400303.

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Mashal Murad Shah, Muhammad Usman Khalid, Mohammad Hamza Bajwa, Farhan A Mirza, Saad bin Anis, Naveed Zaman Akhunzada, Altaf Ali Laghari, Muhammad Faraz Raghib, Sameen Siddiqi, and Syed Ather Enam. "Gender disparities in brain tumours: A Pakistan brain tumour epidemiology study analysis." Journal of the Pakistan Medical Association 72, no. 11 (December 15, 2022): S79—S85. http://dx.doi.org/10.47391/jpma.11-s4-akub13.

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Objective: To explore the differences in demographic, surgical, and prognostic characteristics between the two genders in patients with brain tumours in Pakistan. Methods: This study was a retrospective cross-sectional analysis of patients with a histopathological brain tumour diagnosis across 32 high-volume hospitals in Pakistan. The study period was from January 1, 2019, to December 31, 2019. There were no restrictions on inclusion apart from time. Results: From 2750 patients enrolled in the study, 1605 (58.4%) were male, and 1142 (41.6%) were female . The median age amongst males was 36 (24-49), while the median age amongst females was 37 (24-48). The ratio of married to unmarried patients was 2.7:1 for females and 1.3:1 for males. Surgical treatment was carried out for 1430 (58.1%) males and 1013 (41.9%) females. The median time to surgery was 25 (4-107) days for males and 31 (5-98) days for females. The greatest disparity in tumour malignancy was in grade IV gliomas. Conclusion: Males generally have a higher incidence of brain tumours in our experience, apart from meningiomas, which favour females. The mortality rate and glioblastoma incidence rate are both higher amongst males. However, post-treatment cure is also witnessed. Sociocultural norms play a prominent role in accessing healthcare. Women are generally at a disadvantage compared to their male counterparts, which may impact reporting of brain tumour cases and treatment outcomes. Keywords: Brain neoplasms, Epidemiology, Gender equity, Retrospective study. Continue....

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Fuentes-Fayos, Antonio C., Mari C. Vázquez-Borrego, Juan M. Jiménez-Vacas, Leire Bejarano, Sergio Pedraza-Arévalo, Fernando L.-López, Cristóbal Blanco-Acevedo, et al. "Splicing machinery dysregulation drives glioblastoma development/aggressiveness: oncogenic role of SRSF3." Brain 143, no. 11 (November 2020): 3273–93. http://dx.doi.org/10.1093/brain/awaa273.

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Abstract Glioblastomas remain the deadliest brain tumour, with a dismal ∼12–16-month survival from diagnosis. Therefore, identification of new diagnostic, prognostic and therapeutic tools to tackle glioblastomas is urgently needed. Emerging evidence indicates that the cellular machinery controlling the splicing process (spliceosome) is altered in tumours, leading to oncogenic splicing events associated with tumour progression and aggressiveness. Here, we identify for the first time a profound dysregulation in the expression of relevant spliceosome components and splicing factors (at mRNA and protein levels) in well characterized cohorts of human high-grade astrocytomas, mostly glioblastomas, compared to healthy brain control samples, being SRSF3, RBM22, PTBP1 and RBM3 able to perfectly discriminate between tumours and control samples, and between proneural-like or mesenchymal-like tumours versus control samples from different mouse models with gliomas. Results were confirmed in four additional and independent human cohorts. Silencing of SRSF3, RBM22, PTBP1 and RBM3 decreased aggressiveness parameters in vitro (e.g. proliferation, migration, tumorsphere-formation, etc.) and induced apoptosis, especially SRSF3. Remarkably, SRSF3 was correlated with patient survival and relevant tumour markers, and its silencing in vivo drastically decreased tumour development and progression, likely through a molecular/cellular mechanism involving PDGFRB and associated oncogenic signalling pathways (PI3K-AKT/ERK), which may also involve the distinct alteration of alternative splicing events of specific transcription factors controlling PDGFRB (i.e. TP73). Altogether, our results demonstrate a drastic splicing machinery-associated molecular dysregulation in glioblastomas, which could potentially be considered as a source of novel diagnostic and prognostic biomarkers as well as therapeutic targets for glioblastomas. Remarkably, SRSF3 is directly associated with glioblastoma development, progression, aggressiveness and patient survival and represents a novel potential therapeutic target to tackle this devastating pathology.

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Syed Ather Enam, Sameen Siddiqi, Mohammad Hamza Bajwa, Mashal Murad Shah, and Muhammad Usman Khalid. "Pakistan Brain Tumour Epidemiology Study (PBTES): Uncovering the Hidden Burden of Brain Tumours in the Country." Journal of the Pakistan Medical Association 72, no. 11 (December 15, 2022): S2—S3. http://dx.doi.org/10.47391/jpma.11-s4-akub-e0.

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Epidemiological studies have significantly helped determine the burden, types, and geographical distribution of brain tumours in HICs (highincome countries). However, brain tumour data from low-and-middle-income countries is often sparse, focusing on a few centres1. The situation in Pakistan is no different. Few oncological registries exist in our region that focus on common tumours. Collecting data regarding brain tumours has been challenging, as evidenced by the underreported incidence of brain tumours by centers such as the Karachi Cancer Registry (KCR), Pakistan Atomic Energy Commission (PAEC) report, and GLOBOCAN. Brain tumours are distinct from other cancer types by having more than 200 subtypes, requiring complex analysis, grading, and personalized therapeutic strategies. Moreover, there is no current standardized system to record brain tumour patient data, making it difficult to collate data from various centers. Continue.

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R, Miss Manasa, and Mrs Renuka Malge. "Brain Tumour Classification and Identification Using Deep Learning Neural Networks." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 1598–601. http://dx.doi.org/10.22214/ijraset.2022.45474.

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Abstract: Convolutional Neural Network (CNN)-based brain tumour detection is used to identify and categorise different types of tumours. Many scholars have studied and designed paths across this area over the course of many years. We've suggested a method that can identify and categorise various tumour forms. Since MRI scans the human brain without requiring any operations, they provide a comprehensive picture of the human brain's anatomy, which aids in the processing of the image for tumour identification. Misclassification results from human beings predicting tumours from MRI pictures. This inspires us to create the algorithm for brain tumour identification. For the purpose of identifying tumours, machine learning is helpful and important. Convolutional neural networks (CNNs), one of the machine learning algorithms, was used in this article because of their strength in image processing. Using CNNs and MRI data, we created a web application for the identification of brain tumours and the classification of their various forms, this web application contains about disease, treatments and famous doctors for treat this disease.

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Schankin, CJ, U. Ferrari, VM Reinisch, T. Birnbaum, R. Goldbrunner, and A. Straube. "Characteristics of Brain Tumour-Associated Headache." Cephalalgia 27, no. 8 (August 2007): 904–11. http://dx.doi.org/10.1111/j.1468-2982.2007.01368.x.

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Eighty-five brain tumour patients were examined for further characteristics of brain tumour-associated headache. The overall prevalence of headache in this population was 60%, but headache was the sole symptom in only 2%. Pain was generally dull, of moderate intensity, and not specifically localized. Nearly 40% met the criteria of tension-type headache. An alteration of the pain with the occurrence of the tumour was experienced by 82.5%, implying that the preexisting and the brain tumour headaches were different. The classic characteristics mentioned in the International Classification of Headache Disorders (worsening in the morning or during coughing) were not found; this might be explained by the patients not having elevated intracranial pressure. Univariate analysis revealed that a positive family history of headache and the presence of meningiomas are risk factors for tumour-associated headache, and the use of β-blockers is prophylactic. Pre-existing headache was the only risk factor according to logistic regression, suggesting that patients with pre-existing (primary) headache have a greater predisposition to develop secondary headache. Dull headache occurs significantly more often in patients with glioblastoma multiforme, and pulsating headache in patients with meningioma. In our study, only infratentorial tumours were associated with headache location, and predominantly with occipital but rarely frontal pain.

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Brakel, Benjamin, Chirayu Chokshi, Martin Rossotti, Chitra Venugopal, Sabra Salim, Daniel Mobilio, Shawn Chafe, Kevin Henry, and Sheila Singh. "SYST-15 TARGETING AXONAL GUIDANCE WITH ANTI-ROBO1 CAR T CELLS: A NEW THERAPEUTIC STRATEGY FOR MALIGNANT BRAIN CANCER." Neuro-Oncology Advances 4, Supplement_1 (August 1, 2022): i24. http://dx.doi.org/10.1093/noajnl/vdac078.094.

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Abstract No standardized treatments exist for patients with treatment-refractory brain metastasis, glioblastoma and other recurrent brain tumours. Given the aggressive nature of these diseases and difficulty in modelling tumour recurrence, minimal efforts have been made to design rational therapies against them. Neurodevelopmental pathways are often highjacked and go awry in the progression of these cancers. The roundabout guidance receptor 1 (ROBO1) protein is involved in axonal guidance during neurodevelopment, and we have shown that aberrant ROBO signalling promotes invasiveness and tumour growth in glioblastoma. Likewise, this signalling may contribute to the metastasis and growth of metastatic brain tumours, making the ROBO1-expressing tumour cell population an attractive and functionally relevant therapeutic target. Here, we present that ROBO1 is highly expressed on the surface of malignant and treatment-refractory brain tumour initiating cells (BTICs), prompting the development of an anti-ROBO1 CAR-T cell therapy. Using the binding region of a single-domain antibody targeting ROBO1, we developed second-generation anti-ROBO1 CAR-T cells specific and effective against malignant brain cancers, upregulating markers of activation and degranulation upon exposure to ROBO1-expressing BTICs. Additionally, orthotopic patient-derived xenograft models of malignant brain tumours treated with anti-ROBO1 CAR-T cells had a reduced tumour burden and prolonged survival, demonstrating therapeutic potential for treating brain malignancies.

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Rahman, Jawwad Sami Ur. "Literature Review on Biomedical Imaging Technique for Detection of Brain Tumour." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (February 28, 2020): 1315–23. http://dx.doi.org/10.5373/jardcs/v12sp3/20201380.

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Królikowska, Agnieszka, Piotr Zieliński, Marek Harat, Renata Jabłońska, Beata Haor, Karolina Filipska, and Robert Ślusarz. "The Quality of Life of Patients after Surgical Treatment of Brain Tumours and the Location of the Tumour." Journal of Neurological and Neurosurgical Nursing 9, no. 3 (September 30, 2020): 91–96. http://dx.doi.org/10.15225/pnn.2020.9.3.1.

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Introduction. The location of intracranial neoplasms and the process of treating these lesions itself can significantly affect the quality of life of patients. Hence, the aim of the study was to investigate the impact of the location of the brain tumour on the quality of life of surgically treated patients. Aim. The aim of the study was to investigate the influence of the location of the brain tumour on the quality of life of surgically treated patients. Material and Methods. The study included 236 patients with brain tumours operated at the Department of Neurosurgery of the 10th Military Clinical Hospital with the SP ZOZ Polyclinic in Bydgoszcz. Patients with different tumour locations were included: in the temporal lobe, in the frontal lobe, in the parietal lobe, in the ventricles of the brain and in the extra-cerebral locations. The following questionnaires were used to assess the quality of life: EORTC QLQ-C30 and EORTC QLQ-BN20, in which the patients were tested three times: on the day of admission to the Clinic, on the fifth day after brain tumour surgery and 30 days after the surgery. Results. Patients’ quality of life decreased in the early postoperative period in all groups in terms of tumour location, especially in patients with tumours of the frontal lobe (-0.104) and ventricular neoplasms (-0.109) (p > 0.05). On the 30th day, however, an improvement in the quality of life was achieved in all groups, the highest improvement was obtained in patients with tumours located extra-cerebrally (0.115) and tumours of the temporal lobe (0.097) (p > 0.05). Conclusions. There was no effect of the location of the brain tumour on the quality of life of the studied patients. In the early postoperative period, the quality of life decreased, while it improved 30 days after the surgery. (JNNN 2020;9(3):91–96) Key Words: brain tumour, quality of life, tumour location

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Rasool, Mohammed, Nor Azman Ismail, Wadii Boulila, Adel Ammar, Hussein Samma, Wael M. S. Yafooz, and Abdel-Hamid M. Emara. "A Hybrid Deep Learning Model for Brain Tumour Classification." Entropy 24, no. 6 (June 8, 2022): 799. http://dx.doi.org/10.3390/e24060799.

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A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.

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Sherwood, Matthew, Carolini Kaid, Thiago Mitsugi, Yilu Zhou, Yihua Wang, Paul Skipp, Juliet Gray, Oswaldo Okamoto, and Rob Ewing. "CSIG-29. EMPLOYING THE ZIKA VIRUS AS ONCOLYTIC VIROTHERAPY AGAINST PAEDIATRIC NERVOUS SYSTEM CANCER CELLS." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii45. http://dx.doi.org/10.1093/neuonc/noac209.178.

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Abstract BACKGROUND Malignant paediatric nervous system tumours, such as medulloblastoma, ATRT and high-risk neuroblastoma commonly harbour tumour cells with stem-like features which are highly tumorigenic and resistant to conventional therapies. These tumours can exhibit high lethality and may result in severe sequelae that significantly affect paediatric patients' quality of life. Oncolytic virotherapy exploits viruses that preferentially infect and destroy tumour cells. These viruses present a unique advantage in targeting highly heterogeneous cancers as they possess a secondary mechanism of action, through which they induce an anti-tumoral immune response. The Zika virus (ZIKV) is capable of infecting and destroying aggressive human paediatric brain tumour and neuroblastoma cells in vitro. ZIKV effectively reduces brain tumour size in mice (xenograft model) and canines (naturally occurring) and can induce an immune response against canine brain tumours. METHODS Employing global expression omics profiling of ZIKV infection and mapping of viral protein-host protein interactions, we aim to elucidate the mechanisms which underpin ZIKVs therapeutic properties, both at the molecular and cellular pathway levels. RESULTS Through extensive transcriptome profiling of ZIKV-infected paediatric brain tumour, neuroblastoma and NPCs, we have identified a variety of pathways which are involved in the ZIKV oncolytic response in the tumour cells and its neuro-dysregulation of NPCs. Despite both brain tumour and neuroblastoma cells undergoing ZIKV-induced oncolysis, we observed there to be a heterogeneous response within these different tumour cells at the molecular level to lead to oncolysis. Additionally, the infected tumour cells demonstrate elevated immune system profiles which alludes to the immune response that ZIKV may raise within the patient’s body against the paediatric tumour. Analysing our findings alongside the neuro-dysregulation we observe in our ZIKV-infected NPCs is allowing us to build a safety profile for employing a ZIKV-based therapy, whilst contributing to the growing knowledge of Congenital ZIKV Syndrome.

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Fossdal, Guri, Einar O. Vik-Mo, Cecilie Sandberg, Mercy Varghese, Mari Kaarbø, Emily Telmo, Iver A. Langmoen, and Wayne Murrell. "Aqp 9 and Brain Tumour Stem Cells." Scientific World Journal 2012 (2012): 1–9. http://dx.doi.org/10.1100/2012/915176.

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Several studies have implicated the aquaporins (aqp) 1, 4, and 9 in the pathogenesis of malignant brain tumours, suggesting that they contribute to motility, invasiveness, and oedema formation and facilitate metabolism in tumour cells under hypoxic conditions. We have studied the expression of aqp1, 4, and 9 in biopsies from glioblastomas, isolated tumour stem cells grown in a tumoursphere assay and analyzed the progenitor and differentiated cells from these cultures. We have compared these to the situation in normal rat brain, its stem cells, and differentiated cells derived thereof. In short, qPCR in tumour tissue showed presence of aqp1, 4, and 9. In the tumour progenitor population, aqp9 was markedly more highly expressed, whilst in tumour-derived differentiated cells, aqp4 was downregulated. However, immunostaining did not reveal increased protein expression of aqp9 in the tumourspheres containing progenitor cells; in contrast, its expression (both mRNA and protein) was high in differentiated cultures. We, therefore, propose that aquaporin 9 may have a central role in the tumorigenesis of glioblastoma.

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Nayak, Ashok, Sajith Babu S. M., and Lal Mani Singh. "Immunological monitoring of brain tumour patients." International Surgery Journal 5, no. 5 (April 21, 2018): 1681. http://dx.doi.org/10.18203/2349-2902.isj20181422.

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Background: Patient suffering from CNS tumours are among the best suited as regards the study of their immunologic status is concerned because these tumours rarely metastasize and general condition of patient is not much affected. Extensive research has been done on immunological response in neoplasms of other organs, but immunology of CNS tumours studied mainly during last five decades. It is now realized that immunologic reactions may be important in the development and growth of the CNS tumours. Although there is evidence that immunotherapy is helpful in control of some solid tumours but adequate knowledge of the immunology of glial tumours to guide the rational treatment is not yet available. Methods: This study was conducted on 60 cases that included 20 controls and 40 patients of primary intracranial brain tumors admitted to neurosurgical services of University Hospital, Banaras Hindu University, Varanasi during the period of January 1987 to January 1988. Results: The study revealed, medulloblastoma and glioblastoma or anaplastic astrocytoma show more marked suppression of cell mediated immunity than astrocytoma grade +II and other malignant tumour subgroups. In case of humoral immune response, antigen of brain tumours elicit an Ig M response rather IgG response which is commonly elicited in other neoplasia.Conclusions: The results regarding Ig M, and Ig G levels are in agreement with most of the other studies. It appears that antigen of brain tumours elicits an Ig M response rather IgG response which is commonly elicited in other neoplasia.

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Arvanitis, Costas D., Gino B. Ferraro, and Rakesh K. Jain. "The blood–brain barrier and blood–tumour barrier in brain tumours and metastases." Nature Reviews Cancer 20, no. 1 (October 10, 2019): 26–41. http://dx.doi.org/10.1038/s41568-019-0205-x.

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Roncaroli, Federico, Zhangjie Su, Karl Herholz, Alexander Gerhard, and Federico E. Turkheimer. "TSPO expression in brain tumours: is TSPO a target for brain tumour imaging?" Clinical and Translational Imaging 4, no. 2 (March 22, 2016): 145–56. http://dx.doi.org/10.1007/s40336-016-0168-9.

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Zhang, Liwei, Wang Jia, Nan Ji, Deling Li, Dan Xiao, Guang-Liang Shan, Tao Wang, and Xiong Xiao. "Construction of the National Brain Tumor Registry of China for better management and more efficient use of data: a protocol." BMJ Open 11, no. 1 (January 2021): e040055. http://dx.doi.org/10.1136/bmjopen-2020-040055.

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IntroductionBrain tumours encompass a complex group of intracranial tumours that mostly affect young adults and children, with a high incidence rate and poor prognosis. It remains impossible to systematically collect data on patients with brain tumours in China and difficult to perform in-depth analysis on the status of brain tumours, medical outcomes or other important medical issues through a multicentre clinical study. This study describes the first nation-wide data platform including the entire spectrum of brain tumour entities, which will allow better management and more efficient application of patient data in China.Methods and analysisThe National Brain Tumor Registry of China (NBTRC) is a registry of real-word clinical data on brain tumours. It is established and managed by the China National Clinical Research Center for Neurological Diseases and administered by its scientific and executive committees. The 54 participating hospitals of the NBTRC are located in 27 provinces/municipalities, performing more than 40 000 brain tumour surgeries per year. The data consist of in-hospital medical records, images and follow-up information after discharge. Data can be uploaded in three ways: the web portal, remote physical servers and offline software. The data quality control scheme is seven-dimensional. Each participating hospital could focus on a single pathology subtype and public subtypes of brain tumour for which they expect to conduct related multicentre clinical research. The standardised workflow to conduct clinical research is based on the benefit-sharing mechanism. Data collection will be conducted continuously from 1 February 2019 to 31 January 2024.Ethics and disseminationInformed consent will be obtained from all participants. Consent for the adolescents’ participation will be also obtained from their guardians via written consent. The results will be published in professional journals, in both Chinese and English.Trial registration numberChinese Clinical Trial Registry (ChiCTR1900021096).

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Suveges, Szabolcs, Kismet Hossain-Ibrahim, J. Douglas Steele, Raluca Eftimie, and Dumitru Trucu. "Mathematical Modelling of Glioblastomas Invasion within the Brain: A 3D Multi-Scale Moving-Boundary Approach." Mathematics 9, no. 18 (September 9, 2021): 2214. http://dx.doi.org/10.3390/math9182214.

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Brain-related experiments are limited by nature, and so biological insights are often limited or absent. This is particularly problematic in the context of brain cancers, which have very poor survival rates. To generate and test new biological hypotheses, researchers have started using mathematical models that can simulate tumour evolution. However, most of these models focus on single-scale 2D cell dynamics, and cannot capture the complex multi-scale tumour invasion patterns in 3D brains. A particular role in these invasion patterns is likely played by the distribution of micro-fibres. To investigate the explicit role of brain micro-fibres in 3D invading tumours, in this study, we extended a previously introduced 2D multi-scale moving-boundary framework to take into account 3D multi-scale tumour dynamics. T1 weighted and DTI scans are used as initial conditions for our model, and to parametrise the diffusion tensor. Numerical results show that including an anisotropic diffusion term may lead in some cases (for specific micro-fibre distributions) to significant changes in tumour morphology, while in other cases, it has no effect. This may be caused by the underlying brain structure and its microscopic fibre representation, which seems to influence cancer-invasion patterns through the underlying cell-adhesion process that overshadows the diffusion process.

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Nandan, B., and Kunjam Nageswara Rao. "An Automated Framework for Brain Tumour Class Detection." International Journal of Engineering & Technology 7, no. 4 (September 24, 2018): 2463. http://dx.doi.org/10.14419/ijet.v7i4.17930.

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With the significant growth in medical imaging techniques and the demand for better processing of medical information, the mandate of automation in disease detection is also increasing. In the modern time, the nature of the diseases has also changed. The highly mortal diseases are becoming difficult to detect due to the high involvements of medical individual and high dependency of human knowledges. The human knowledge is prone to error and often criticized for longer time delay for processing information in disease detections. Thus, the demand from the modern computing and implementation based computational algorithms are to automate the medical disease detection processes with greater accuracy. One such disease with superior mortal rate is brain tumours or cancerous growth in the brain tissues. The regular medical practice approaches have demonstrated the challenges in detection of the tumours and more so the nature of the tumours. Ill detection of the tumour type or the shape of the tumour or the size of the tumours can lead to life threats. Thus, the need for automation in detection is the most expected form of replacements in place of manual diagnosis. Another challenge is the available data formats for such disease reports. The available reports for brain tumour are only in the form of magnetic resonance images or MR Images. The MR Images can cause higher obstacles for further processing as due to the capture process of the patient data. Often, it is observed that the noise present in the MR images makes the processing vulnerable in accuracy. A number of parallel research outcomes have demonstrated significant outcomes of detection of available tumours in the human brain using segmentation methods. Nonetheless, all parallel attempts are criticized for not able to model the growth or the nature of the tumours presents in the human brain. Thus, this work proposes a novel automated framework for detection of tumour types by deploying progressive segmentation and model the growth stages based on features. The parallel outcomes have outrun on detection accuracy due to the use of standard segmentation methods, which is designed for generic image processing and bound not to match the specificity of medical image processing. Thus, this work introduces a novel segmentation method, which is progressive in nature for higher accuracy. This work also outcomes into an automated feature extraction model for brain tumours. The major contribution of the work is to determine the nature of tumour and a sustainable prediction model for tumour stages inside the human brain. The work demonstrates high accuracy for correct detection and prediction of the patient’s life threats in in real time order to take timely medication for making the precious human life more precious.

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Brooks, D. J., R. P. Beaney, D. G. T. Thomas, J. Marshall, and T. Jons. "Studies on Regional Cerebral pH in Patients with Cerebral Tumours Using Continuous Inhalation of 11CO2 and Positron Emission Tomography." Journal of Cerebral Blood Flow & Metabolism 6, no. 5 (October 1986): 529–35. http://dx.doi.org/10.1038/jcbfm.1986.98.

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Regional cerebral pH (rpH) was measured in 12 patients with cerebral tumours and in 5 normal subjects using continuous inhalation of 11CO2 and positron emission tomography (PET). Cerebral tumours with a disrupted blood–brain barrier (BBB) on computed tomography scanning had a similar rpH to that of equivalent regions of contralateral brain tissue (mean tumour rpH, 6.98; mean contralateral brain pH, 6.99). Cerebral tumours with an intact BBB were consistently found to be more alkaline than contralateral brain tissue (mean tumour rpH, 7.09). There was no significant difference between the mean rpH values obtained for peripheral cortical gray and central white matter in normal subjects (7.02 and 6.98, respectively). It is concluded that in spite of reports of raised levels of aerobic glycolysis in neoplastic tissue, there is no evidence that cerebral tumour rpH values are depressed.

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Jeshwanth, M., Aditya Raghav, Rudresh Deepak Shirwaikar, Abu Mohammed Faisal, and Kuthika Ramesh. "3D segmentation of brain tumour." International Journal of Engineering Systems Modelling and Simulation 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijesms.2022.10047159.

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Tripathi, Jyotsana. "BRAIN TUMOUR DETECTION AND SEGMENTATION." International Journal of Advanced Research in Computer Science 9, no. 3 (June 20, 2018): 165–69. http://dx.doi.org/10.26483/ijarcs.v9i3.6091.

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Khalequezzaman, Syed, and Taslima Rahman. "A Rare Brain Tumour-Gliosarcoma." Medicine Today 28, no. 1 (January 3, 2017): 46–47. http://dx.doi.org/10.3329/medtoday.v28i1.30972.

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A 57-year-old gentleman presented with a history of leftsided weakness and loss of appetite. Brain MRI was suggestive of right frontal and thalamic mass lesion with contrast enhancement at the periphery. Open biopsy examination revealed a malignant brain tumour presenting a biphasic tissue pattern with gliomatous and mesenchymal components suggestive of gliosarcoma. Although the treatment of gliosarcomas is almost similar to glioblastomas (surgical resection and depending on clinical status, radiotherapy and/or chemotherapy) the prognosis of gliosarcomas remains poor.Medicine Today 2016 Vol.28(1): 46-47

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Petrik, Vladimir, Alison Loosemore, Franklyn A. Howe, B. Anthony Bell, and Marios C. Papadopoulos. "OMICS and brain tumour biomarkers." British Journal of Neurosurgery 20, no. 5 (January 2006): 275–80. http://dx.doi.org/10.1080/02688690600999620.

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PLOWMAN, P. N. "EDITORIAL Paediatric brain tumour therapy." British Journal of Neurosurgery 10, no. 1 (January 1996): 5–7. http://dx.doi.org/10.1080/02688699650040467.

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PALMIERI, RACHEL L. "Responding to primary brain tumour." Nursing 37, no. 1 (January 2007): 36–42. http://dx.doi.org/10.1097/00152193-200701000-00034.

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Ferreira, A., A. Henriques, and L. Pereira. "1272 Brain tumour in childhood." European Journal of Cancer Supplements 1, no. 5 (September 2003): S385. http://dx.doi.org/10.1016/s1359-6349(03)91298-5.

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Goonawardena, Janindu, Laurence A. G. Marshman, and Katharine J. Drummond. "Brain tumour-associated status epilepticus." Journal of Clinical Neuroscience 22, no. 1 (January 2015): 29–34. http://dx.doi.org/10.1016/j.jocn.2014.03.038.

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Doz, F., S. V. Picton, S. Rutkowski, J. C. Nicholson, D. Frappaz, D. Hargrave, M. Frühwald, H. L. Muller, and L. Gandola. "99 SIOP brain tumour trials." European Journal of Cancer Supplements 7, no. 2 (September 2009): 26. http://dx.doi.org/10.1016/s1359-6349(09)70092-8.

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Bydder, Sean. "Invasive brain tumour after radiosurgery." Lancet 357, no. 9259 (March 2001): 887. http://dx.doi.org/10.1016/s0140-6736(05)71824-9.

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Yu, John S., and Keith L. Black. "Invasive brain tumour after radiosurgery." Lancet 357, no. 9259 (March 2001): 887–88. http://dx.doi.org/10.1016/s0140-6736(05)71825-0.

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Yumitori, K., H. Handa, T. Teraura, J. Yamashita, and K. Yamamura. "Metastatic brain tumour and fibrinopeptides." Acta Neurochirurgica 89, no. 1-2 (March 1987): 43–47. http://dx.doi.org/10.1007/bf01406666.

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Journal articles on the topic 'Brain tumour'

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