Radiation-Induced Meningiomas After Childhood Brain Tumor: A Magnetic Resonance Imaging Screening Study

2019 ◽  
Vol 8 (5) ◽  
pp. 593-601 ◽  
Author(s):  
Tiina M. Remes ◽  
Maria H. Suo-Palosaari ◽  
Vesa-Pekka Heikkilä ◽  
Anna K. Sutela ◽  
Päivi K. T. Koskenkorva ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Childhood Brain Tumor ◽  
Screening Study ◽  
Radiation Induced ◽  
Magnetic Resonance Imaging Screening

scholarly journals Medulloblastoma with Excessive Nodularity: Radiographic Features and Pathologic Correlate

2012 ◽  
Vol 2012 ◽  
pp. 1-3 ◽  
Author(s):  
L. A. Yeh-Nayre ◽  
D. M. Malicki ◽  
D. N. Vinocur ◽  
J. R. Crawford
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Posterior Fossa ◽  
Medical Management ◽  
Resonance Imaging ◽  
Childhood Brain Tumor ◽  
Favorable Prognosis ◽  
Weighted Magnetic Resonance Imaging ◽  
Aggressive Medical Management

Medulloblastoma with extensive nodularity is a rare subtype of the most common malignant childhood brain tumor and has been associated with more favorable prognosis. The authors report the case of a 10-month-old girl with a posterior fossa tumor of excessive nodularity with decreased diffusivity on diffusion-weighted magnetic resonance imaging sequences and robust grape-like postgadolinium contrast enhancing features. The unique neuroradiographic features were confirmed by histopathology and a diagnosis of medulloblastoma with extensive nodularity was made. This case highlights the importance of recognizing this unique medulloblastoma subtype preoperatively, as the more favorable outcome may preclude less aggressive medical management.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

Multiple myeloma manifesting as an intraventricular brain tumor

2009 ◽  
Vol 110 (4) ◽  
pp. 737-739 ◽  
Author(s):  
Joo-Hun David Eum ◽  
Astrid Jeibmann ◽  
Werner Wiesmann ◽  
Werner Paulus ◽  
Heinrich Ebel
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Myeloma ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Lateral Ventricle ◽  
Histopathological Examination ◽  
Brain Parenchyma ◽  
Resonance Imaging

Primary intracerebral manifestation of multiple myeloma is rare and usually arises from the meninges or brain parenchyma. The authors present a case of multiple myeloma primarily manifesting within the lateral ventricle. A 67-year-old man was admitted with headache accompanied by slowly progressing right hemiparesis. Magnetic resonance imaging showed a large homogeneous contrast-enhancing intraventricular midline mass and hydrocephalus. The tumor was completely resected, and histopathological examination revealed plasmacytoma. After postoperative radio- and chemotherapy, vertebral osteolysis was detected as a secondary manifestation of multiple myeloma.

scholarly journals Perfusion Magnetic Resonance Imaging to Assess Brain Tumor Responses to New Therapies

1998 ◽  
Vol 5 (2) ◽  
pp. 115-123 ◽  
Author(s):  
Michael H. Lev ◽  
Fred Hochberg
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Imaging Techniques ◽  
Perfusion Mri ◽  
Perfusion Magnetic Resonance Imaging ◽  
Normal Brain ◽  
Resonance Imaging ◽  
New Therapies ◽  
Magnetic Resonance Imaging Mri

Background: Although magnetic resonance imaging (MRI) is effective in detecting the location of intracranial tumors, new imaging techniques have been studied that may enhance the specificity for the prediction of histologic grade of tumor and for the distinction between recurrence and tumor necrosis associated with cancer therapy. Methods: The authors review their experience and that of others on the use of perfusion magnetic resonance imaging to evaluate responses of brain tumors to new therapies. Results: Functional imaging techniques that can distinguish tumor from normal brain tissue using physiological parameters. These new approaches provide maps of tumor perfusion to monitor the effects of novel compounds that restrict tumor angiogenesis. Conclusions: Perfusion MRI not only may be as effective as radionuclide-based techniques in sensitivity and specificity in assessing brain tumor responses to new therapies, but also may offer higher resolution and convenient co-registration with conventional MRI, as well as time- and cost-effectiveness. Further study is needed to determine the role of perfusion MRI in assessing brain tumor responses to new therapies.

Magnetic Resonance Imaging Brain Tumor Image Segmentation Based on Improved U-Net

2021 ◽  
Vol 58 (4) ◽  
pp. 0410022
Author(s):  
牟海维 Mu Haiwei ◽  
郭颖 Guo Ying ◽  
全星慧 Quan Xinghui ◽  
曹志民 Cao Zhimin ◽  
韩建 Han Jian
Keyword(s):  
Magnetic Resonance Imaging ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Resonance Imaging
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