scholarly journals Mastodynia

CES Medicina ◽  
2019 ◽  
Vol 33 (2) ◽  
pp. 118-118
Author(s):  
Óscar Alejandro Bonilla-Sepulveda ◽  
Diana Carolina Giraldo-Santa
Keyword(s):  
Breast Cancer ◽  
Chest Pain ◽  
Literature Search ◽  
Breast Pain ◽  
Therapeutic Implications ◽  
Single Mechanism ◽  
Mesh Terms ◽  
Concept Classification ◽  
Classification And Treatment

Introduction: Mastalgia is a frequent clinical symptom in general and gynecological medicine consultation. The objectives of this bibliographical review are: to define the diagnostic concept, classification and treatment of mastalgia. Methods: literature search in Pubmed, Scielo, Cochrane and Google Scholar, using the MeSH terms: mastodynia, mastalgia, breast pain, chest pain. The search limits were: full texts in English and Spanish, in humans and published during the last 10 years. Results: the physiopathology of the mastodynia is not caused by a single mechanism, but by different causes with independent pathways. The main mechanisms are hormonal, mammary density, neuropathic and extra-mammary. Conclusion: It is important to know the pathophysiology and classification of mastodynia, due to its therapeutic implications and possible association with breast cancer.

OP611 Breast Cancer Classification In Histopathological Images Using Artificial Intelligence Assisted Diagnosis Technology: A Meta-Analysis

2020 ◽  
Vol 36 (S1) ◽  
pp. 15-15
Author(s):  
Guo Huang ◽  
Di Xue
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Diagnostic Accuracy ◽  
Literature Search ◽  
Meta Analysis ◽  
Cancer Classification ◽  
Breast Cancer Classification ◽  
Histopathological Images ◽  
Assisted Diagnosis

IntroductionArtificial Intelligence (AI) is an important product of the rapid development of computer technology today. It has a far-reaching impact on the development of medical diagnostic technology especially in combination with medical imaging. The aim of this study was to analyze the diagnostic accuracy of AI-assisted diagnosis technology for classification of breast cancer in histopathological images.MethodsA meta-analysis was conducted of published research articles on diagnostic accuracy of AI-assisted diagnosis technology for breast cancer classification between January 2010 and September 2019 in the databases of PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform and China Bio-medicine Database. Statistical analysis was performed with software Meta-Disc 1.4 and Stata 12.0, and the summary receiver operating characteristic (SROC) curve was drawn to evaluate accuracy of the method.ResultsA total of 18 studies with 13,573 breast histopathological images were considered for the analysis. The pooled sensitivity, specificity, diagnostic odds ratio and the area under the curve of the SROC for AI-assisted diagnosis technology for classification of breast cancer respectively, were 0.94 (95% confidence interval [CI]: 0.93–0.85), 0.84 (95% CI: 0.93–0.94), 255.47 (95% CI: 168.33–387.73) and 0.98 (95%CI: 0.96–0.99).ConclusionsSeveral limitations should be considered when interpreting the findings of this meta-analysis. First, despite the extensive literature search, the number of included studies was small; however, the number of images enrolled was satisfactory, thereby decreasing type II error. Second, data acquisition is not comprehensive enough because the language of literature search was limited to Chinese and English. Furthermore, the heterogeneity caused due to different sources of data affected the study results. Despite these limitations, our study suggests AI-assisted diagnosis technology for breast cancer classification in histopathological images is a highly accurate and reliable diagnostic method for clinical application.

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

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

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

2020 ◽  
Vol 16 ◽  
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.

The Impact of Translational Research in Breast Cancer Care: Can we Improve the Therapeutic Scenario?

2018 ◽  
Vol 18 (6) ◽  
pp. 832-836
Author(s):  
Giuseppe Buono ◽  
Francesco Schettini ◽  
Francesco Perri ◽  
Grazia Arpino ◽  
Roberto Bianco ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Translational Research ◽  
Cell Biology ◽  
Cancer Biology ◽  
Hormone Receptors ◽  
Treatment Strategies ◽  
Growth Factor Receptor ◽  
Molecular Heterogeneity ◽  
Therapeutic Implications ◽  
The Impact

Traditionally, breast cancer (BC) is divided into different subtypes defined by immunohistochemistry (IHC) according to the expression of hormone receptors and overexpression/amplification of human epidermal growth factor receptor 2 (HER2), with crucial therapeutic implications. In the last few years, the definition of different BC molecular subgroups within the IHC-defined subtypes and the identification of the important role that molecular heterogeneity can play in tumor progression and treatment resistance have inspired the search for personalized therapeutic approaches. In this scenario, translational research represents a key strategy to apply knowledge from cancer biology to the clinical setting, through the study of all the tumors “omics”, including genomics, transcriptomics, proteomics, epigenomics, and metabolomics. Importantly, the introduction of new high-throughput technologies, such as next generation sequencing (NGS) for the study of cancer genome and transcriptome, greatly amplifies the potential and the applications of translational research in the oncology field. Moreover, the introduction of new experimental approaches, such as liquid biopsy, as well as new-concept clinical trials, such as biomarker-driven adaptive studies, may represent a turning point for BC translational research. </P><P> It is likely that translational research will have in the near future a significant impact on BC care, especially by giving us the possibility to dissect the complexity of tumor cell biology and develop new personalized treatment strategies.

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

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.

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

1994 ◽  
Vol 1 (2) ◽  
pp. 49-55 ◽  
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.

scholarly journals Recent Discoveries of Macromolecule- and Cell-Based Biomarkers and Therapeutic Implications in Breast Cancer

2021 ◽  
Vol 22 (2) ◽  
pp. 636
Author(s):  
Hsing-Ju Wu ◽  
Pei-Yi Chu
Keyword(s):  
Breast Cancer ◽  
Normal Breast ◽  
New Drugs ◽  
Cancer Type ◽  
Luminal A ◽  
Luminal B ◽  
Therapeutic Implications ◽  
Whole Cells ◽  
Diagnosis And Prognosis ◽  
Novel Biomarkers

Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality in women worldwide. Breast cancer is fairly heterogeneous and reveals six molecular subtypes: luminal A, luminal B, HER2+, basal-like subtype (ER−, PR−, and HER2−), normal breast-like, and claudin-low. Breast cancer screening and early diagnosis play critical roles in improving therapeutic outcomes and prognosis. Mammography is currently the main commercially available detection method for breast cancer; however, it has numerous limitations. Therefore, reliable noninvasive diagnostic and prognostic biomarkers are required. Biomarkers used in cancer range from macromolecules, such as DNA, RNA, and proteins, to whole cells. Biomarkers for cancer risk, diagnosis, proliferation, metastasis, drug resistance, and prognosis have been identified in breast cancer. In addition, there is currently a greater demand for personalized or precise treatments; moreover, the identification of novel biomarkers to further the development of new drugs is urgently needed. In this review, we summarize and focus on the recent discoveries of promising macromolecules and cell-based biomarkers for the diagnosis and prognosis of breast cancer and provide implications for therapeutic strategies.

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