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819 results for “Brain Tumor”
Longitudinal observational study of pediatric patients with primary brain tumors: establishment of a hospital-based registry.
<p>Although tumors of the central nervous system (CNS) represent 2 % of all malignancies in general, they cause a disproportionately large morbidity and mortality and are the second most common form of cancer in children and the major solid tumor in childhood in the U.S., occurring in 21.3% of all children with malignant disease. The treatment of brain tumors in children and adolescents has evolved significantly in recent decades. Nowadays, most children with a diagnosis of brain tumor are treated properly and achieve prolonged survival. In order to obtain an overview of the impact of brain tumors, specialized registries, which provide information on all types of brain tumors, have emerged in several countries. Following on the pioneering Japanese and American experiences of specialized national records of brain tumors, other specialized registries were opened in European countries. This project aims to initiate a registry of the epidemiological profile of patients treated for CNS tumors in the Pediatric Cancer Center (CPC) of our hospital from January 2000 to December 2013, at diagnosis and during follow-up, updating information periodically. This data will be recorded in an electronic database capable of storing, retrieving and presenting information of interest. Prospectively recorded epidemiological data of patients diagnosed from January 2014 will be accrued, maintaining the database active to continuously record information on patients with CNS tumors treated in the CPC HIAS. Thus, creating a hospital registry of pediatric patients with CNS tumors. To this end, an instrument of data collection will be created using Google Apps (Google Inc., 2014), a digital platform with capacity for storage, creation and editing of documents and collaboration in real time over the cloud.</p>
Brain Tumor MR Image Data Set For Machine Vision Approach for Brain Tumor Classification using Multi Features Dataset
<p>The uploaded dataset contains the brain tumor MRI dataset. The dataset has been collected form the Bahawal Victoria Hospital, Bahawalpur, Pakistan. This dataset is an authorized MRI brain tumor dataset. Is has been authorized from the expert Radiologists of the Bahawal Victoria Hospital <a href="https://www.qamc.edu.pk/administration/2">BVH</a>. The dataset consists of three brain tumor types, namely adenomas, meningioma and glioma. it is only for academic, educational and experimental purpose. no other usage will be owned or any liability will be accepted by the authors.</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 4.Performance based on no. of tumor pixel & execution time
<p>In this paper we segmented the brain tumors in axial view of MR images with the help of<br> unsupervised clustering method i.e. K-means clustering. The unsupervised clustering methods gave<br> the better results than traditional method.<br> The performance analysis and comparison is done f on the basis of no. of tumor pixels in<br> segmented brain tumor and the execution time for the same. Regarding the no. of tumor pixels, Kmeans<br> clustering gave a better result than the other methods. The clustering algorithms were tested<br> with a data base of 20 MRI brain images. K-means clustering achieved almost 90%result</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 3:(a) Input MR Image (b) Enhanced Image (c) Segmented Tumor (d) Located brain tumor
<p>Figure 3 shows three different original brain MR images, contrast enhancement of the<br> images, segmented images using K-means algorithm and finally located tumor. Fig 1.4 shows the<br> performance of the unsupervised clustering methods with the no. of tumor pixels and execution<br> time to locate the brain tumor.</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 2. Stages of software implementation
<p>The algorithm has two stages, first is pre-processing of given MRI image and after that<br> segmentation and then perform morphological operations.</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 1. Diagnosis Rate in different Countrie
<p>In MRI images, the amount of data is too much for manual segmentation. The procedure is<br> tedious, time, labor consuming, subjective and requires expertise. This gave way to methods that are<br> computer-aided with user interaction at varying levels. These methods are automatic and objective<br> and the results are highly reproducible. We designed software tool for locating brain tumor, based<br> on unsupervised clustering methods and analyzed its performance</p>
A dataset of brain tumor simulations
<p>We release a dataset of 30k numerical simulations of brain tumors modelled in brain atlas. </p> <p>The tumor model is based on reaction-diffusion partial differential equation. The details about the model can be found in [1], and the brain atlas is taken from [2]. </p> <p>Each folder contains a tumor simulation in <code>Data_0001_thr2.npz</code> file and corresponding tumor model parameters in <code>parameter_tag2.pkl</code> file.</p> <p> </p> <p>References:</p> <p>[1] Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling, Ezhov I. et al., Medical Image Analysis </p> <p>[2] The SRI24 multichannel atlas of normal adult human brain structure, Rohlfing T. et al., Human Brain Mapping </p>
DATASET RELATED TO ARTICLE "Brain Tumor Resection in Elderly Patients Potential Factors of Postoperative Worsening in a Predictive Outcome Model"
<p><span>clinical database including information on patients included in the study at title</span></p>
Impact of CD4 T cells on intratumoral CD8 T cell exhaustion and responsiveness to PD-1 blockade therapy in mouse brain tumors
<p>scRNA-seq data (Cellranger filtered feature-barcode matrices) and scVDJ-seq data (Cellranger filtered_contig_annotations.csv files) for publication listed above.</p>
brain-tumor-mri-dataset
<p>Brain Tumor MRI Dataset from Kaggle: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset</p> <p>Author: Msoud Nickparvar</p>
Brain Tumor Paper Dataset and Code
<p><strong>Brain Tumor Detection Research Paper Code and Dataset</strong></p> <p><strong>Paper title:</strong> <em>Transforming brain tumor detection: the impact of YOLO models and MRI orientations.</em></p> <p><strong>Authored by:</strong> <em>Yazan Al-Smadi, Ahmad Al-Qerem, et al. </em>(2023)</p> <p><br> This project contains a full version of the used brain tumor dataset and a full code version of the proposed research methodology.</p>
Tamoxifen and Bortezomib to Treat Recurrent Brain Tumors
ClinicalTrials.gov study NCT00108069. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Safety Study of Aminolevulinic Acid (ALA) to Enhance Visualization and Resection of Tumors of the Brain
ClinicalTrials.gov study NCT01116661. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Immersive Virtual Reality (VR) at the Time of Clinical Evaluation to Improve Psychological Distress and Anxiety in Primary Brain Tumor (PBT) Patients
ClinicalTrials.gov study NCT04301089. IPD Sharing: YES. Countries: 1. Publications: 7.
Proton Versus Photon Radiotherapy in Adults With Primary Brain Tumors
ClinicalTrials.gov study NCT06831461. IPD Sharing: NO. Countries: 1. Publications: 0.
Weight Loss Study for Patients With Obesity Due to Craniopharyngioma or Other Brain Tumor
ClinicalTrials.gov study NCT01484873. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study of Pomalidomide Monotherapy for Children and Young Adults With Recurrent or Progressive Primary Brain Tumors
ClinicalTrials.gov study NCT03257631. IPD Sharing: Not stated. Countries: 5. Publications: 2.
Memantine in Preventing Side Effects in Patients Undergoing Whole-Brain Radiation Therapy for Brain Metastases From Solid Tumors
ClinicalTrials.gov study NCT00566852. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Safety and Efficacy Study of Tarceva, Temodar, and Radiation Therapy in Patients With Newly Diagnosed Brain Tumors
ClinicalTrials.gov study NCT00187486. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Cilengitide (EMD 121974) for Recurrent Glioblastoma Multiforme (Brain Tumor)
ClinicalTrials.gov study NCT00093964. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.