Audit of histomorphology and immunohistochemistry of the brain tumors: Revisited in context to the WHO 2016 molecular classification

2017 ◽  
Vol 2 (1) ◽  
pp. 15
Author(s):  
Tanushri Mukherjee ◽  
Rajat Dutta ◽  
Joydeep Ghosh
Keyword(s):  
Social Sciences ◽  
Statistical Analysis ◽  
Brain Tumors ◽  
Molecular Classification ◽  
Histopathological Diagnosis ◽  
Treatment Change ◽  
Tumor Patients ◽  
The Brain

<p><span class="Bold">Background:</span><span> The WHO 2016 molecular classification corroborating with the histology has given more significant diagnostic objectivity to the diagnosis of brain tumors and it is more reliable for instituting therapy as the heterogeneity and observer subjectivity are bypassed with the addition of isocitrate dehydrogenase, ATRX, and 1p19q, and other molecular markers. </span><span class="Bold">Aim:</span><span> Our aim is to review the histopathology of diagnosed brain tumors and correlate with immunohistochemical (IHC) findings to note for any disparity to reform the diagnosis in order to benefit the patient and report to the clinician if any treatment change is to be considered. </span><span class="Bold">Materials and Methods:</span><span> This article is based on studies of screening and diagnostic test. A total of 150 brain tumors were retrospectively analyzed. Age, gender, and the tumor histological type and grade were systematically recorded. We compared our histopathological diagnosis before the introduction of the WHO 2016 molecular classification of central nervous system tumors and later after the relevant IHC and fluorescence </span><span class="Italic">in situ</span><span> hybridization studies. </span><span class="Bold">Statistical Analysis:</span><span> The statistical analysis was done by using Statistical Package for Social Sciences version recent for Windows. </span><span class="Bold">Results:</span><span> Out of the total 150 brain tumor patients, 65 were males and 45 were females. About 37 were glial and the rest were in other categories. </span><span class="Bold">Conclusions:</span><span> </span><span lang="en-US">The molecular diagnosis that substantiated with the histomorphology is more objective and beneficial in the treatment of the patients.</span></p>

scholarly journals Classification of Tumors in Brain MRI Images With Hybrid of Global and Local DWT Features using Decision Tree

2019 ◽  
Vol 8 (12) ◽  
pp. 3072-3077
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Decision Tree ◽  
Brain Mri ◽  
Extraction Process ◽  
Discrete Wavelet ◽  
Global Features ◽  
Tumor Identification ◽  
The Brain

Automated brain tumor identification and classification is still an open problem for research in the medical image processing domain. Brain tumor is a bunch of unwanted cells that develop in the brain. This growth of a tumor takes up space within skull and affects the normal functioning of brain. Automated segmentation and detection of brain tumors are important in MRI scan analysis as it provides information about neural architecture of brain and also about abnormal tissues that are extremely necessary to identify appropriate surgical plan. Automating this process is a challenging task as tumor tissues show high diversity in appearance with different patients and also in many cases they tend to appear very similar to the normal tissues. Effective extraction of features that represent the tumor in brain image is the key for better classification. In this paper, we propose a hybrid feature extraction process. In this process, we combine the local and global features of the brain MRI using first by Discrete Wavelet Transformation and then using texture based statistical features by computing Gray Level Co-occurrence Matrix. The extracted combined features are used to construct decision tree for classification of brain tumors in to benign or malignant class.

Indeterminacy in the WHO classification of tumors: an example of the histopathological diagnosis of brain tumors

2011 ◽  
Vol 28 (3) ◽  
pp. 247-251 ◽  
Author(s):  
Gaku Tanaka ◽  
Yoichi Nakazato ◽  
Tarou Irié ◽  
Tatsuya Okada ◽  
Masafumi Abe
Keyword(s):  
Brain Tumors ◽  
Who Classification ◽  
Histopathological Diagnosis ◽  
Classification Of Tumors

scholarly journals Advances in the molecular classification of pediatric brain tumors: a guide to the galaxy

2020 ◽  
Vol 251 (3) ◽  
pp. 249-261 ◽  
Author(s):  
Chantel Cacciotti ◽  
Adam Fleming ◽  
Vijay Ramaswamy
Keyword(s):  
Brain Tumors ◽  
Molecular Classification ◽  
Pediatric Brain Tumors ◽  
The Galaxy ◽  
Pediatric Brain

Insights About Brain Tumors Gained Through Immunohistochemistry and in Situ Hybridization of Nuclear and Phenotypic Markers

1997 ◽  
Vol 3 (S2) ◽  
pp. 13-14
Author(s):  
P.E. McKeever
Keyword(s):  
Brain Tumors ◽  
Malignant Gliomas ◽  
Growth Potential ◽  
Primary Antibody ◽  
Specific Marker ◽  
Nuclear Markers ◽  
Cellular Markers ◽  
Abc Method

Immunohistochemistry (IHC) has provided major insights about the classification of brain tumors by identifying cellular markers of phenotype, and about tumor growth potential with nuclear markers of proliferation. Newer in situ hybridization shows promise in tumor classification and prognostication.Fig. 1 shows the sensitive and reliable avidin-biotin conjugate (ABC) method of localizing glial fibrillary acidic protein (GFAP). GFAP is the most specific marker of gliomas, tumors of brain cells, available today. The ABC method can be used to find any antigen for which a primary antibody is available. A molecular bridge then links this primary antibody bound to tissue with a label that can be seen, a substrate of the horseradish peroxidase (HRP) enzyme like diaminobenzidine (DAB) that produces a brown, insoluble reaction product at the site of the antigen. IHC for GFAP has revealed that brain tumors previously thought to be sarcomas are actually malignant gliomas (Fig. 2).

scholarly journals Cytological Evaluations of Advanced Generations of Interspecific Hybrids Between Allium cepa and Allium fistulosum Showing Resistance to Stemphylium vesicarium

Genes ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 195 ◽  
Author(s):  
Natalia Kudryavtseva ◽  
Michael J. Havey ◽  
Lowell Black ◽  
Peter Hanson ◽  
Pavel Sokolov ◽  
...  
Keyword(s):  
Allium Cepa ◽  
Interspecific Hybrids ◽  
Molecular Classification ◽  
Allium Fistulosum ◽  
Bulb Onion ◽  
Allium Cepa L ◽  
Disease Resistances ◽  
Interspecific Crossing

Interspecific crossing is a promising approach for introgression of valuable traits to develop cultivars with improved characteristics. Allium fistulosum L. possesses numerous pest resistances that are lacking in the bulb onion (Allium cepa L.), including resistance to Stemphylium leaf blight (SLB). Advanced generations were produced by selfing and backcrossing to bulb onions of interspecific hybrids between A. cepa and A. fistulosum that showed resistance to SLB. Molecular classification of the cytoplasm established that all generations possessed normal (N) male−fertile cytoplasm of bulb onions. Genomic in situ hybridization (GISH) was used to study the chromosomal composition of the advanced generations and showed that most plants were allotetraploids possessing the complete diploid sets of both parental species. Because artificial doubling of chromosomes of the interspecific hybrids was not used, spontaneous polyploidization likely resulted from restitution gametes or somatic doubling. Recombinant chromosomes between A. cepa and A. fistulosum were identified, revealing that introgression of disease resistances to bulb onion should be possible.

Measurement Methodologies for Assessing the Glycolysis Effect in the Discrimination and Therapy of Brain Gliomas

2013 ◽  
Vol 1 (1) ◽  
pp. 34-55 ◽  
Author(s):  
Michalis G Kounelakis ◽  
Ekaterini S Bei ◽  
Michalis E Zervakis ◽  
Georgios C Giakos ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Molecular Classification ◽  
Scientific Basis ◽  
World Health ◽  
Primary Brain Tumors ◽  
Glycolysis Pathway ◽  
Health Organization ◽  
Tumor Types ◽  
Brain Gliomas ◽  
The Brain

Primary brain tumors refer to those developing from the various types of cells that compose the brain. Gliomas represent about 50% of all primary brain tumors and include a variety of different histological tumor types and malignancy grades. The World Health Organization (WHO) classifies gliomas into four histological types and four grades. The goal of molecular classification using advanced pattern recognition tools is to identify subgroups of tumors with distinct biological and clinical features and initiate the challenge of classifying complex gliomas of similar histology and malignancy status into distinct categories. The aim of this paper is to i) present the measurement procedures and analysis methodologies, ii) summarize the currently available knowledge related to the utilization of ’omics’ measurements in the discrimination of brain gliomas, and iii) provide a scientific basis for future medical practice in the discrimination and treatment of brain gliomas based specifically on the metabolic process of glycolysis. In particular, the paper explores the idea of the glycolysis pathway as a critical concept for the development of therapeutic strategies for brain gliomas.

scholarly journals Molecular Classification of Breast Carcinoma In Situ

2006 ◽  
Vol 7 (8) ◽  
pp. 523-532 ◽  
Author(s):  
Usha Raju ◽  
Mei Lu ◽  
Seema Sethi ◽  
Hina Qureshi ◽  
Sandra Wolman ◽  
...  
Keyword(s):  
Breast Carcinoma ◽  
Carcinoma In Situ ◽  
Molecular Classification ◽  
Breast Carcinoma In Situ

scholarly journals Performance Analysis of Deep Belief Neural Network for Brain Tumor Classification

2020 ◽  
Vol 1 (3) ◽  
pp. 29-34
Author(s):  
Sreenivas Eeshwaroju ◽  
◽  
Praveena Jakula ◽  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Imaging Techniques ◽  
Structural Heterogeneity ◽  
Low Complexity ◽  
Automated Classification ◽  
Tumor Identification ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

The brain tumors are by far the most severe and violent disease, contributing to the highest degree of a very low life expectancy. Therefore, recovery preparation is a crucial step in improving patient quality of life. In general , different imaging techniques such as computed tomography ( CT), magnetic resonance imaging ( MRI) and ultrasound imaging have been used to examine the tumor in the brain, lung , liver, breast , prostate ... etc. MRI images are especially used in this research to diagnose tumor within the brain with classification results. The massive amount of data produced by the MRI scan, therefore, destroys the manual classification of tumor vs. non-tumor in a given period. However for a limited number of images, it is presented with some constraint that is precise quantitative measurements. Consequently, a trustworthy and automated classification scheme is important for preventing human death rates. The automatic classification of brain tumors is a very challenging task in broad spatial and structural heterogeneity of the surrounding brain tumor area. Automatic brain tumor identification is suggested in this research by the use of the classification with Deep Belief Network (DBN). Experimental results show that the DBN archive rate with low complexity seems to be 97 % accurate compared to all other state of the art methods.

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