Basal cytokeratins and their relationship to the cellular origin and functional classification of breast cancer

Barry A Gusterson; Douglas T Ross; Victoria J Heath; Torsten Stein

doi:10.1186/bcr1041

S5 The evolution of pathology in breast cancer: Relations between cellular origin and functional classification

The Breast ◽

10.1016/s0960-9776(07)70028-0 ◽

2007 ◽

Vol 16 ◽

pp. S1-S2

Author(s):

J. Palacios

Keyword(s):

Breast Cancer ◽

Functional Classification ◽

Cellular Origin

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Objective and parametric methods used in functional classification disabled swimmers

Physiotherapy ◽

10.2478/physio-2013-0022 ◽

2013 ◽

Vol 21 (3) ◽

Cited By ~ 1

Author(s):

Natalia Uścinowicz ◽

Wojciech Seidel ◽

Paweł Zostawa ◽

Sebastian Klich

Keyword(s):

Medical Diagnosis ◽

Olympic Games ◽

Objective Evaluation ◽

Subjective Assessment ◽

Fair Play ◽

Functional Classification ◽

Kinematic Parameters ◽

Athletes With Disabilities ◽

Rule Out

AbstractThe recent Olympic Games in London incited much interest in the competition of disabled athletes. Various people connected with swimming, including coaches and athletes, have speculated about the fairness of competitions of disabled athletes. A constant problem are the subjective methods of classification in disabled sport. Originally, athletes with disabilities were classified according to medical diagnosis. Due to the injustice which still affects the competitors, functional classification was created shortly after. In the present review, the authors show the anomalies in the structure of the classification. The presented discovery led to the suggestion to introduce objective methods, thanks to which it would be no longer necessary to rely on the subjective assessment of the classifier. According to the authors, while using objective methods does not completely rule out the possibility of fraud by disabled athletes in the classification process, it would certainly reduce their incidence. Some of the objective methods useful for the classification of disabled athletes are: posturography, evaluation of the muscle parameters, electrogoniometric assessment, surface electromyography, and analysis of kinematic parameters. These methods have provide objective evaluation in the diagnostic sense but only if they are used in tandem. The authors demonstrate the undeniable benefits of using objective methods. Unfortunately, there are not only advantages of such solution, there also several drawbacks to be found. The conclusion of the article is the statement by the authors that it is right to use objective methods which allow to further the most important rule in sport: fair-play.

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Classification of Breast Cancer Resistant Protein (BCRP) Inhibitors and Non-Inhibitors Using Machine Learning Approaches

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207318666150525094503 ◽

2015 ◽

Vol 18 (5) ◽

pp. 476-485 ◽

Cited By ~ 7

Author(s):

Vilas Belekar ◽

Karthik Lingineni ◽

Prabha Garg

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Learning Approaches ◽

Breast Cancer Resistant Protein

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Model for Classification of ChEMBL Dataset of Anti-breast cancer Compounds According to Multiple Pharmacological Parameters, Biological Assays, and Receptors

Letters in Drug Design & Discovery ◽

10.2174/1570180813666151109201317 ◽

2015 ◽

Vol 13 (999) ◽

pp. 1-1

Author(s):

Francisco J. Prado-Prado ◽

Angel G. Arguello-Chan ◽

Coraima I. Estrada-Domínguez ◽

Alejandra Aguirre-Crespo ◽

Francisco J. Aguirre-Crespo ◽

...

Keyword(s):

Breast Cancer ◽

Biological Assays

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Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666200406110547 ◽

2020 ◽

Vol 16 ◽

Cited By ~ 3

Author(s):

Saliha Zahoor ◽

Ikram Ullah Lali ◽

Muhammad Attique Khan ◽

Kashif Javed ◽

Waqar Mehmood

Keyword(s):

Breast Cancer ◽

Computer Aided Diagnosis ◽

Diagnose Breast Cancer ◽

Initial Stage ◽

Computer Aided ◽

Diagnosis And Classification ◽

And Performance ◽

Tools And Techniques ◽

Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

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A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

Recent Patents on Engineering ◽

10.2174/1872212114999201109205421 ◽

2020 ◽

Vol 14 ◽

Author(s):

Lahari Tipirneni ◽

Rizwan Patan

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Convolutional Neural Network ◽

Binary Classification ◽

Disease Diagnosis ◽

Feature Descriptors ◽

Histopathological Images ◽

Viable Approach ◽

Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

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Hormonal characterization and classification of breast cyst fluid in gross cystic mastopathy

Endocrine Related Cancer ◽

10.1677/erc.0.0010049 ◽

1994 ◽

Vol 1 (2) ◽

pp. 49-55 ◽

Cited By ~ 2

Author(s):

I Számel ◽

B Budai ◽

K Daubner ◽

J Kralovánszky ◽

Sz Ottó ◽

...

Keyword(s):

Breast Cancer ◽

Sex Steroids ◽

Linear Regression Analysis ◽

Cyst Fluid ◽

Type I ◽

Breast Cyst Fluid ◽

Breast Cyst ◽

Apocrine Metaplasia ◽

Endocrine Parameters

ABSTRACT Gross cystic disease (GCD) of the breast may be associated with a higher risk for the development of breast cancer. High levels of sex steroids, steroid hormone precursors, prolactin and cations have been found in breast cyst fluid (BCF) by several investigators. Accordingly, endocrine parameters and the cationic composition of BCF may be considered as useful characteristics to follow patients bearing macrocysts. In this study we have investigated the concentrations of estradiol (E2), progesterone, testosterone, dehydroepiandrosterone (DHA) and DHA-3-sulfate (DHA-S), prolactin, potassium (K+) and sodium (Na+) in BCF aspirated from 99 women. The mean age of the patients was 49.8 years (range 32-58). The hormone levels were measured by RIA methods; K+ and Na+ were determined by flame photometry. Estradiol, progesterone, testosterone, DHA, DHA-S, prolactin and K+ showed significant accumulation in the BCF compared with their respective serum values. The K+/Na+ ratio proved to be useful in dividing cysts into type I (≥1), type II (<1 but ≥0.1) and type III (<0.1) subgroups. For type I BCF, higher DHA, DHA-S and prolactin concentrations were detected. Linear regression analysis established a highly significant (P<0.001) correlation between the concentrations of E2 and DHA-S (r=0.686), and also between testosterone and DHA-S (r=0.711). These findings indicate that type I BCF might be a marker for 'active' GCD of the breast, and suggest that it may be associated with an increased breast cancer risk, since this group of patients is supposed to have cysts with apocrine metaplasia. It is suggested therefore that when BCF is aspirated, sex steroids, steroid precursors and cations should be routinely measured, and women with type I cysts should be regularly examined.

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