Breast cancer identification based on artificial intelligent system

2020 ◽  
Vol 2 (2) ◽  
pp. 41-49
Author(s):  
Hassan Khalil Silman ◽  
Akbas Ezaldeen Ali
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Intelligent System ◽  
High Rate ◽  
Computer Assisted ◽  
Fine Needle Aspirates ◽  
Manual Intervention ◽  
Breast Tissue Density ◽  
Breast Tissues ◽  
Background Area

Worldwide, breast cancer causes a high mortality rate. Early diagnosis is important for treatment, but high density breast tissues are difficult to analyze. Computer-assisted identification systems were introduced to classify is fine needle aspirates (fna) , with features that better represent the images to be classified as a major challenge. This work is fully automated, and it does not require any manual intervention from user. In this analysis, various texture definitions for the portrayal of breast tissue density on mammograms are examined within addition to contrasting them with other techniques. We have created an algorithm that can be divided into three classes: fatty, fatty-glandular and dense-glandular, The suggested system works in a spatial-related domain and it results extremely immunity to noise and background area, with a high rate of precision.

scholarly journals Breast cancer identification based on artificial intelligent system

2020 ◽  
Vol 2 (2) ◽  
pp. 109-118
Author(s):  
Hassan Khalil Silman ◽  
Akbas Ezaldeen Ali
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Intelligent System ◽  
High Rate ◽  
Computer Assisted ◽  
Fine Needle Aspirates ◽  
Manual Intervention ◽  
Breast Tissue Density ◽  
Breast Tissues ◽  
Background Area

Worldwide, breast cancer causes a high mortality rate. Early diagnosis is important for treatment, but high-density breast tissues are difficult to analyze. Computer-assisted identification systems were introduced to classify by fine needle aspirates FNA with features that better represent the images to be classified as a major challenge. This work is fully automated, and it does not require any manual intervention from user. In this analysis, various texture definitions for the portrayal of breast tissue density on mammograms are examined within addition to contrasting them with other techniques. We have created an algorithm that can be divided into three classes: fatty, fatty-glandular and dense-glandular. The suggested system works in a spatial-related domain and it results with extreme immunity to noise and background area, with a high rate of precision.

Outcomes after Surgery for Early Stage Breast Cancer in Women Staged With Preoperative Breast Magnetic Resonance Imaging According to Breast Tissue Density

2019 ◽  
Vol 1 (2) ◽  
pp. 115-121
Author(s):  
Renata Faermann ◽  
Jonathan Weidenfeld ◽  
Leonid Chepelev ◽  
Wayne Kendal ◽  
Raman Verma ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Early Stage ◽  
Disease Free Survival ◽  
Breast Mri ◽  
Early Stage Breast Cancer ◽  
Free Survival ◽  
Tissue Density ◽  
Breast Tissue Density ◽  
Preoperative Breast Mri

Abstract Purpose To determine surgical outcomes and breast cancer disease-free survival outcomes of women with early stage breast cancer with and without use of preoperative breast MRI according to breast tissue density. Methods Women with early stage breast cancer diagnosed from 2004 to 2009 were classified into 2 groups: 1) those with dense and heterogeneously dense breasts (DB); 2) those with nondense breasts (NDB) (scattered fibroglandular and fatty replaced tissue). The 2 groups were reviewed to determine who underwent preoperative MRI. Breast tissue density was determined with mammography according to ACR BI-RADS. Patients were compared according to tumor size, grade, stage, and treatment. Survival analysis was performed using Kaplan-Meier estimates. Results In total, 261 patients with mean follow-up of 85 months (25–133) were included: 156 DB and 105 NDB. Disease-free survival outcomes were better in the DB group in patients with MRI than in those without MRI: patients with MRI had significantly fewer local recurrences (P < 0.016) and metachronous contralateral breast cancers (P < 0.001), but this was not the case in the NDB group. Mastectomies were higher in the DB group with preoperative MRI than in those without MRI (P < 0.01), as it was in the NDB group (P > 0.05). Conclusions Preoperative breast MRI was associated with reduced local recurrence and metachronous contralateral cancers in the DB group, but not in the NDB group; however, the DB patients with MRI had higher mastectomy rates.

An Intelligent-Based Wavelet Classifier for Accurate Prediction of Breast Cancer

2018 ◽  
pp. 306-319 ◽  
Author(s):  
Anandakumar Haldorai ◽  
Arulmurugan Ramu
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Roc Curve ◽  
Breast Tissue ◽  
Wavelet Neural Network ◽  
Clinical Database ◽  
New Approach ◽  
X Ray ◽  
Breast Tissues

The detection of cancer in the breast is done using mammograms (x-ray images). The authors propose a CAD framework for distinguishing little changes in mammogram which may demonstrate malignancies which are too little to be felt either by the lady herself or by a radiologist. In this chapter, they build up a framework for analysis, visualization, and prediction of cancer in breast tissue by utilizing Intelligent based wavelet classifier. Intelligent-based wavelet classifier is a new approach constructed using texture value and wavelet neural network. The proposed framework is applied to the genuine clinical database of 160 mammograms gathered from mammogram screening focuses. The execution of the CAD framework is examined utilizing ROC curve. This will help the specialists in determination of the breast tissues either cancerous or noncancerous in an accurate way.

scholarly journals Genomic Changes in Normal Breast Tissue in Women at Normal Risk or at High Risk for Breast Cancer

2016 ◽  
Vol 10 ◽  
pp. BCBCR.S39384 ◽  
Author(s):  
David N. Danforth
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Breast Tissue ◽  
Sporadic Breast Cancer ◽  
Normal Breast Tissue ◽  
Normal Breast ◽  
Breast Carcinogenesis ◽  
Molecular Changes ◽  
Breast Tissues

Sporadic breast cancer develops through the accumulation of molecular abnormalities in normal breast tissue, resulting from exposure to estrogens and other carcinogens beginning at adolescence and continuing throughout life. These molecular changes may take a variety of forms, including numerical and structural chromosomal abnormalities, epigenetic changes, and gene expression alterations. To characterize these abnormalities, a review of the literature has been conducted to define the molecular changes in each of the above major genomic categories in normal breast tissue considered to be either at normal risk or at high risk for sporadic breast cancer. This review indicates that normal risk breast tissues (such as reduction mammoplasty) contain evidence of early breast carcinogenesis including loss of heterozygosity, DNA methylation of tumor suppressor and other genes, and telomere shortening. In normal tissues at high risk for breast cancer (such as normal breast tissue adjacent to breast cancer or the contralateral breast), these changes persist, and are increased and accompanied by aneuploidy, increased genomic instability, a wide range of gene expression differences, development of large cancerized fields, and increased proliferation. These changes are consistent with early and long-standing exposure to carcinogens, especially estrogens. A model for the breast carcinogenic pathway in normal risk and high-risk breast tissues is proposed. These findings should clarify our understanding of breast carcinogenesis in normal breast tissue and promote development of improved methods for risk assessment and breast cancer prevention in women.

scholarly journals Therapeutic Targets In Breast Cancer Signaling: A Review

2021 ◽  
pp. 82-99
Author(s):  
Emmanuel Ifeanyi Obeagu ◽  
Quratulain Babar ◽  
C. C. N. Vincent ◽  
Chikwendu Lawrence Udenze ◽  
Richard Eze ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Predictive Biomarkers ◽  
Molecular Therapy ◽  
Therapeutic Interventions ◽  
Breast Cancers ◽  
Dominant Type ◽  
Cellular Pathways ◽  
Breast Tissues ◽  
Human Epidermal Receptor

For women, the most dominant type of cancer is breast cancer and perhaps one of the most recognizedreasons of death. This is a disorder of many distinct traits, many of which are known as positive hormone receptor, human epidermal receptor-2 (HER2+), and three negative breast cancers (TNBC). Drugs that directly target and kill tumors constitute a rapidly-growing form of molecular therapy for cancer patients. Analysis reveals that stable breast tissue cells exhibit receptors which aren't usually present. As a result, it is imperative to cognize the molecular roots of breast cancer and the myriad compromised pathology-related processes and pathways to ensure progresses in early diagnosis and prevention. This study demonstrates essential cellular pathways relevant for breast cancer including improvements in cell proliferation, apoptosis, and hormone balances in breast tissues. On the basis of these notions, we consider how breast cancer is associated to the creation of potentially therapeutic interventions and predictive biomarkers.

scholarly journals A hybrid feature selection on AIRS method for identifying breast cancer diseases

2021 ◽  
Vol 11 (1) ◽  
pp. 728
Author(s):  
Achmad Ridok ◽  
Nashi Widodo ◽  
Wayan Firdaus Mahmudy ◽  
Muhaimin Rifa’i
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Breast Tissue ◽  
Information Gain ◽  
Classification Problem ◽  
Accurate Identification ◽  
Fine Needle Aspirates ◽  
Cell Pattern ◽  
Invasive Method ◽  
Cancer Diseases

Breast cancer may cause a death due to the late diagnosis. A cheap and accurate tool for early detection of this disease is essential to prevent fatal incidence. In general, the cheap and less invasive method to diagnose the disease could be done by biopsy using fine needle aspirates from breast tissue. However, rapid and accurate identification of the cancer cell pattern from the cell biopsy is still challenging task. This diagnostic tool can be developed using machine learning as a classification problem. The performance of the classifier depends on the interrelationship between sample sizes, some features, and classifier complexity. Thus, the removal of some irrelevant features may increase classification accuracy. In this study, a new hybrid feature selection fast correlation based feature (FCBF) and information gain (IG) was used to select features on identifying breast cancer using AIRS algorithm. The results of 10 times the crossing (CF) of our validation on various AIRS seeds indicate that the proposed method can achieve the best performance with accuracy =0.9797 and AUC=0.9777 at k=6 and seed=50.

scholarly journals Microbiome composition indicate dysbiosis and lower richness in tumor breast tissues compared to healthy adjacent paired tissue, within the same women

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Maria Valeria Esposito ◽  
Bruno Fosso ◽  
Marcella Nunziato ◽  
Giorgio Casaburi ◽  
Valeria D’Argenio ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Microbial Composition ◽  
Bacterial Composition ◽  
Therapeutic Implications ◽  
Microbiome Composition ◽  
Paired Samples ◽  
Number Of Factors ◽  
Tumor Tissues ◽  
Breast Tissues

Abstract Background Breast cancer (BC) is the most common malignancy in women, in whom it reaches 20% of the total neoplasia incidence. Most BCs are considered sporadic and a number of factors, including familiarity, age, hormonal cycles and diet, have been reported to be BC risk factors. Also the gut microbiota plays a role in breast cancer development. In fact, its imbalance has been associated to various human diseases including cancer although a consequential cause-effect phenomenon has never been proven. Methods The aim of this work was to characterize the breast tissue microbiome in 34 women affected by BC using an NGS-based method, and analyzing the tumoral and the adjacent non-tumoral tissue of each patient. Results The healthy and tumor tissues differed in bacterial composition and richness: the number of Amplicon Sequence Variants (ASVs) was higher in healthy tissues than in tumor tissues (p = 0.001). Moreover, our analyses, able to investigate from phylum down to species taxa for each sample, revealed major differences in the two richest phyla, namely, Proteobacteria and Actinobacteria. Notably, the levels of Actinobacteria and Proteobacteria were, respectively, higher and lower in healthy with respect to tumor tissues. Conclusions Our study provides information about the breast tissue microbial composition, as compared with very closely adjacent healthy tissue (paired samples within the same woman); the differences found are such to have possible diagnostic and therapeutic implications; further studies are necessary to clarify if the differences found in the breast tissue microbiome are simply an association or a concausative pathogenetic effect in BC. A comparison of different results on similar studies seems not to assess a universal microbiome signature, but single ones depending on the environmental cohorts’ locations.

scholarly journals Aberrant epigenetic and transcriptional events associated with breast cancer risk

2021 ◽  
Author(s):  
Natascia Marino ◽  
Rana German ◽  
Ram Podicheti ◽  
Douglas B. Rush ◽  
Pam Rockey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Epithelial Cells ◽  
Cancer Risk ◽  
Breast Tissue ◽  
Cancer Susceptibility ◽  
Breast Cancer Susceptibility ◽  
Normal Breast ◽  
Breast Tissues

ABSTRACTBackgroundGenome-wide association studies have identified several breast cancer susceptibility loci. However, biomarkers for risk assessment are still missing. Here, we investigated cancer-related molecular changes detected in tissues from women at high risk for breast cancer prior to disease manifestation. Disease-free breast tissue cores donated by healthy women (N=146, median age=39 years) were processed for both methylome (MethylCap) and transcriptome (Illumina’s HiSeq4000) sequencing. Analysis of tissue microarray and primary breast epithelial cells was used to confirm gene expression dysregulation.ResultsTranscriptomic analysis identified 69 differentially expressed genes between women at either high and those at average risk of breast cancer (Tyrer-Cuzick model) at FDR<0.05 and fold change≥2. The majority of the identified genes were involved in DNA damage checkpoint, cell cycle, and cell adhesion. Two genes, FAM83A and NEK2, were overexpressed in tissue sections (FDR<0.01) and primary epithelial cells (p<0.05) from high-risk breasts. Moreover, 1698 DNA methylation aberrations were identified in high-risk breast tissues (FDR<0.05), partially overlapped with cancer-related signatures and correlated with transcriptional changes (p<0.05, r≤0.5). Finally, among the participants, 35 women donated breast biopsies at two time points, and age-related molecular alterations enhanced in high-risk subjects were identified.ConclusionsNormal breast tissue from women at high risk of breast cancer bears molecular aberrations that may contribute to breast cancer susceptibility. This study is the first molecular characterization of the true normal breast tissues and provides an opportunity to investigate molecular markers of breast cancer risk, which may lead to new preventive approaches.

ESR1 and GPX1 genes expression level in human malignant and non-malignant breast tissues

2018 ◽  
Vol 65 (1) ◽  
pp. 51-57 ◽  
Author(s):  
Magdalena Beata Król ◽  
Michał Galicki ◽  
Peter Grešner ◽  
Edyta Wieczorek ◽  
Ewa Jabłońska ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Breast Tissue ◽  
Mrna Level ◽  
Clinicopathological Factors ◽  
Down Regulation ◽  
Expression Levels ◽  
Genes Expression ◽  
Malignant Breast ◽  
Breast Tissues

Background: The aim of this study was to find out whether the mRNA expression of estrogen receptor alpha (encoded by ESR1) correlates with the expression of glutathione peroxidase 1 (encoded by GPX1) in tumor and adjacent tumor-free breast tissue and whether this correlation is affected by breast cancer. Such relationships may give further insights into breast cancer pathology with respect to the status of estrogen receptor. Methods: We used the quantitative real-time PCR technique to analyze differences in the expression levels of the ESR1 and GPX1 genes in paired malignant and non-malignant tissues from breast cancer patients. Results: ESR1 and GPX1 expression levels were found to be significantly down-regulated by 14.7% and 7.4% (respectively) in the tumorous breast tissue compared to the non-malignant one. Down-regulation of these gene were independent of tumor histopathology classification and clinicopathological factors while ESR1 mRNA level was reduced with increasing tumor grade (G1: 103% vs. G2: 85.8% vs. G3: 84.5%; p<0.05). In the non-malignant and malignant breast tissues, the expression levels of ESR1 and GPX1 were significantly correlated with each other (Rs=0.450 and Rs=0.360; respectively). Conclusion: These data suggest that down-regulation of ESR1 and GPX1 are independent on clinicopathological factors. Down-regulation of ESR1 gene expression enhanced with the development of the disease. Moreover, GPX1 and ESR1 genes expression are interdependent in the malignant breast tissue and further work is needed to determine the mechanism underlying this relationship.

scholarly journals Differential Expression of Blood Group Precursor Antigen in Human Breast Cancer Tissue

2020 ◽  
pp. 1-3
Author(s):  
Denong Wang ◽  
Yi Jiang ◽  
Denong Wang
Keyword(s):  
Breast Cancer ◽  
Blood Group ◽  
Breast Tissue ◽  
Tissue Microarrays ◽  
Breast Cancer Tissue ◽  
Cancer Tissue ◽  
Human Breast ◽  
Human Breast Cancer Tissue ◽  
Breast Tissues ◽  
Conserved Epitope

There is a pressing need for biomarkers for targeted immunotherapy against breast cancer (BCA), the leading cause of cancer death in women. Previously, a blood group precursor O-core epitope gpC1 was found to be highly expressed in breast circulating tumor cells (BCTCs) and BCA cell lines with cancer stem cell (BCSC) features. In this pilot study, the breast tissue distribution of gpC1 was examined using tissue microarrays (TMAs). Notably, gpC1 positive cells were detected in the major histological types of neoplastic breast tissues. Conversely, none of the breast tissues derived from subjects without BCA were gpC1 positive. Thus, gpC1 expression seems to be tumor-specific but not histological type-dependent, reflecting perhaps its characteristics as a conserved epitope of oncofetal blood group precursor antigens.

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