Analysis of Risk Factors for Breast Cancer Decision Support System in Egypt

2017 ◽  
Vol 6 (1) ◽  
pp. 23-31
Author(s):  
Basma Emad Abd El-Fatah ◽  
Mohamed I. Owis ◽  
Manal Abdel Wahed
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Feature Selection ◽  
Risk Factor ◽  
Second Phase ◽  
Selection Methods ◽  
Economic Method ◽  
False Diagnosis ◽  
Two Phases ◽  
Phase Normal

Breast cancer is the most common form of cancer among women. A risk factor is anything that influences the possibility of obtaining a disease as cancer. The goal of this study is creating economic method for the early detection of breast cancer using risk factors. Two approaches were followed by applying classification techniques. In the first approach, benign, malignant and normal were classified. The second approach comprised two phases. In the first phase, normal and tumor cases were detected, then in the second phase benign and malignant cases were detected. Risk factors were ranked by two different feature selection methods. The best result accuracy of the first phase of the second approach was 96.7%. This could help in the detection of normal and tumor cases without mammography giving a fast diagnosing result, training of non-expert doctors and reducing mammography harms and false diagnosis of inexperienced doctors.

scholarly journals Future Burden of Cancer Attributable to Current Modifiable Behaviours: A Pooled Study of Seven Australian Cohorts

2018 ◽  
Vol 4 (Supplement 2) ◽  
pp. 5s-5s
Author(s):  
M.A. Laaksonen ◽  
M.E. Arriaga ◽  
K. Canfell ◽  
R.J. MacInnis ◽  
P. Hull ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Factor ◽  
Alcohol Intake ◽  
Breast Cancers ◽  
Premenopausal Breast Cancer ◽  
Current Smoking ◽  
Cancer Burden ◽  
The Future ◽  
Future Burden

Background: The Population Attributable Fraction (PAF) quantifies the fraction of cancer cases attributable to specific exposures. PAF estimates for the future burden of cancer preventable through modifications to current exposure distributions are lacking. Previous PAF studies have also not compared population subgroup differences. Aim: To apply a novel PAF method and i) assess the future burden of cancer in Australia preventable through modifications to current behaviors, and ii) compare the distribution of the preventable cancer burden between population subgroups. Methods: We harmonized and pooled data from seven Australian cohort studies (N=367058) and linked them to national registries to identify cancers and deaths. We estimated the strength of the associations between behaviors and cancer incidence and death using a proportional hazards model, adjusting for age, sex, study and other risk factors. Exposure prevalence was estimated from contemporary national health surveys. We then combined these estimates to calculate PAFs and their 95% confidence intervals for both individual and joint behavior modifications using a novel method accounting for competing risk of death and risk factor interdependence. We also compared PAFs between population subgroups by calculating the 95% confidence interval of the difference in PAF estimates. Results: During the first 10 years of follow-up, there were 22078 deaths and 27483 incident cancers, including 2025 lung, 3471 colorectal, 640 premenopausal and 2632 postmenopausal breast cancers. The leading preventable cause for lung cancer is current smoking (PAF = 53.7%), for colorectal and postmenopausal breast cancer body fatness or BMI ≥ 25 kg/m2 (PAF = 11.1% and 10.9% respectively), and for premenopausal breast cancer regular alcohol intake (PAF = 12.3%). Three in five lung cancers, but only one in five colorectal and breast cancers, are jointly attributable to potentially modifiable exposures, which also included physical inactivity and inadequate fruit intake for lung, excessive alcohol intake and current smoking for colorectal, regular alcohol intake and current menopausal hormone therapy for 1 year or more for postmenopausal breast and current oral contraceptive use for 5 years or more for premenopausal breast cancer. The cancer burden attributable to modifiable factors is markedly higher in certain population subgroups, including men (lung, colorectal), people with risk factor clustering (lung, colorectal, breast), and individuals with low educational attainment (lung, breast). Conclusion: We provided up-to-date estimates of the future Australian cancer burden attributable to modifiable risk factors, and identified population subgroups that experience the highest preventable burden. Application of the novel PAF method can inform timely public health action to improve health and health equity, by identifying those with the most to gain from programs that support behavior change and early detection.

scholarly journals Prevalence of germline BRCA mutations in HER2-negative metastatic breast cancer: global results from the real-world, observational BREAKOUT study

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Joyce O’Shaughnessy ◽  
Christine Brezden-Masley ◽  
Marina Cazzaniga ◽  
Tapashi Dalvi ◽  
Graham Walker ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Ovarian Cancer ◽  
Risk Factor ◽  
Metastatic Breast Cancer ◽  
Family History ◽  
Metastatic Breast ◽  
Cytotoxic Chemotherapy ◽  
First Line ◽  
History Of

Abstract Background The global observational BREAKOUT study investigated germline BRCA mutation (gBRCAm) prevalence in a population of patients with human epidermal growth factor receptor 2 (HER2)-negative metastatic breast cancer (MBC). Methods Eligible patients had initiated first-line cytotoxic chemotherapy for HER2-negative MBC within 90 days prior to enrollment. Hormone receptor (HR)-positive patients had experienced disease progression on or after prior endocrine therapy, or endocrine therapy was considered unsuitable. gBRCAm status was determined using baseline blood samples or prior germline test results. For patients with a negative gBRCAm test, archival tissue was tested for somatic BRCAm and homologous recombination repair mutations (HRRm). Details of first-line cytotoxic chemotherapy were also collected. Results Between March 2017 and April 2018, 384 patients from 14 countries were screened and consented to study enrollment; 341 patients were included in the full analysis set (median [range] age at enrollment: 56 [25–89] years; 256 (75.3%) postmenopausal). Overall, 33 patients (9.7%) had a gBRCAm (16 [4.7%] in gBRCA1 only, 12 [3.5%] in gBRCA2 only, and 5 [1.5%] in both gBRCA1 and gBRCA2). gBRCAm prevalence was similar in HR-positive and HR-negative patients. gBRCAm prevalence was 9.0% in European patients and 10.6% in Asian patients and was higher in patients aged ≤ 50 years at initial breast cancer (BC) diagnosis (12.9%) than patients aged > 50 years (5.4%). In patients with any risk factor for having a gBRCAm (family history of BC and/or ovarian cancer, aged ≤ 50 years at initial BC diagnosis, or triple-negative BC), prevalence was 10.4%, versus 5.8% in patients without these risk factors. HRRm prevalence was 14.1% (n = 9/64) in patients with germline BRCA wildtype. Conclusions Patient demographic and disease characteristics supported the association of a gBRCAm with younger age at initial BC diagnosis and family history of BC and/or ovarian cancer. gBRCAm prevalence in this cohort, not selected on the basis of risk factors for gBRCAm, was slightly higher than previous results suggested. gBRCAm prevalence among patients without a traditional risk factor for harboring a gBRCAm (5.8%) supports current guideline recommendations of routine gBRCAm testing in HER2-negative MBC, as these patients may benefit from poly(ADP-ribose) polymerase (PARP) inhibitor therapy. Trial registration NCT03078036.

Breast cancer epidemiologic risk factors by HER2/neu status

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 585-585
Author(s):  
W. Y. Chen ◽  
G. A. Colditz ◽  
B. Rosner
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Factor ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Benign Breast Disease ◽  
Breast Disease ◽  
Breast Cancers ◽  
Cancer Risk Factors ◽  
Breast Cancer Risk Factors

585 Background: Although breast cancers categorized by estrogen receptor (ER) and progesterone receptor (PR) status are recognized to differ in their associations with standard breast cancer risk factors, little data exist on differences by HER2/neu status. Methods: The Nurses’ Health Study is a prospective cohort study of 121,700 registered nurses aged 30–55 in 1976 who answered biennial questionnaires to update medical and lifestyle factors and disease occurrence. Medical record review was used to confirm invasive breast cancer and abstract ER, PR, and HER2/neu status. Statistical analyses included both proportional hazards models to estimate relative risks and control for potential confounders and polytomous logistic regression to compare the effects. Only cases diagnosed from return of the 1998 questionnaire until June 2002 were included in the analysis since HER2/neu was only routinely assessed beginning with the 1998 follow-up cycle. Results: 211 HER2/neu positive and 770 HER2/neu negative cases were included in the analysis. In this predominantly postmenopausal group aged 52–77 in 1998, HER2neu negative cancers were more likely to be ER+/PR+ (72%) and less likely to be ER-/PR- (11%) than HER2/neu positive ones (58% ER+/PR+ and 24% ER-/PR-), but the majority of cancers were still ER+/PR+. In multivariate models, risk factor associations by HER2/neu status were similar with positive associations seen for family history, benign breast disease, body mass index, current postmenopausal hormone use, and cumulative alcohol consumption. However, when the subgroup of ER-/PR-/HER2/neu negative cancers were evaluated separately (N=83), most of these risk factor associations disappeared with the only significant risk factor being a prior history of benign breast disease. Conclusions: This is the first prospective data study to report on risk factor association by HER2/neu status. For the standard epidemiologic breast cancer risk factors, ER and PR status appear to better represent separate etiologic pathways, rather than HER2/neu status. However, the subgroup of ER/PR/HER2neu negative breast cancers appears to be distinct, although power was limited and HER2/neu status was not confirmed by central review. Additional analyses stratified by ER/PR status will also be presented. No significant financial relationships to disclose.

scholarly journals Research of Feature Selection Methods to Predict Breast Cancer

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2353-2355 ◽  
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Early Stage ◽  
Classification Methods ◽  
Selection Methods ◽  
Breast Cancer Data ◽  
Cancer Data ◽  
Cancer Breast ◽  
Data Mining Approach ◽  
The World

Human health is most important than anything in the world, one should take care of it. Among various disease, cancer is the most terrible and deadly disease, so it is necessary to predict such disease in early stage. In this paper different feature selection methods used for feature extraction with different feature classification methods to identify the breast cancer. Breast cancer data is taken from UCI repository and is processed using WEKA tool and proposed techniques are applied to classify data accurately. This study well defines that data mining approach is suitable for predicting breast cancer.

scholarly journals Classical and Deep Learning Paradigms for Detection and Validation of Key Genes of Risky Outcomes of HCV

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 73
Author(s):  
Nagwan M. Abdel Samee
Keyword(s):  
Hepatic Cirrhosis ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Second Phase ◽  
Selection Methods ◽  
Neural Network Approach ◽  
Learning Techniques ◽  
Key Genes ◽  
Two Phases

Hepatitis C virus (HCV) is one of the most dangerous viruses worldwide. It is the foremost cause of the hepatic cirrhosis, and hepatocellular carcinoma, HCC. Detecting new key genes that play a role in the growth of HCC in HCV patients using machine learning techniques paves the way for producing accurate antivirals. In this work, there are two phases: detecting the up/downregulated genes using classical univariate and multivariate feature selection methods, and validating the retrieved list of genes using Insilico classifiers. However, the classification algorithms in the medical domain frequently suffer from a deficiency of training cases. Therefore, a deep neural network approach is proposed here to validate the significance of the retrieved genes in classifying the HCV-infected samples from the disinfected ones. The validation model is based on the artificial generation of new examples from the retrieved genes’ expressions using sparse autoencoders. Subsequently, the generated genes’ expressions data are used to train conventional classifiers. Our results in the first phase yielded a better retrieval of significant genes using Principal Component Analysis (PCA), a multivariate approach. The retrieved list of genes using PCA had a higher number of HCC biomarkers compared to the ones retrieved from the univariate methods. In the second phase, the classification accuracy can reveal the relevance of the extracted key genes in classifying the HCV-infected and disinfected samples.

scholarly journals The acceptability of addressing alcohol consumption as a modifiable risk factor for breast cancer: a mixed method study within breast screening services and symptomatic breast clinics

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e027371 ◽  
Author(s):  
Julia Sinclair ◽  
Mark McCann ◽  
Ellena Sheldon ◽  
Isabel Gordon ◽  
Lyn Brierley-Jones ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Health Promotion ◽  
Risk Factor ◽  
Mixed Method ◽  
Breast Screening ◽  
Mixed Method Study ◽  
Alcohol Content ◽  
Teachable Moments ◽  
Symptomatic Breast

ObjectivesPotentially modifiable risk factors account for approximately 23% of breast cancers, with obesity and alcohol being the two greatest. Breast screening and symptomatic clinical attendances provide opportunities (‘teachable moments’) to link health promotion and breast cancer-prevention advice within established clinical pathways. This study explored knowledge and attitudes towards alcohol as a risk factor for breast cancer, and potential challenges inherent in incorporating advice about alcohol health risks into breast clinics and screening appointments.DesignA mixed-method study including a survey on risk factors for breast cancer and understanding of alcohol content. Survey results were explored in a series of five focus groups with women and eight semi-structured interviews with health professionals.SettingWomen attending NHS Breast Screening Programme (NHSBSP) mammograms, symptomatic breast clinics and healthcare professionals in those settings.Participants205 women were recruited (102 NHSBSP attenders and 103 symptomatic breast clinic attenders) and 33 NHS Staff.ResultsAlcohol was identified as a breast cancer risk factor by 40/205 (19.5%) of attenders and 16/33 (48.5%) of staff. Overall 66.5% of attenders drank alcohol, and 56.6% could not estimate correctly the alcohol content of any of four commonly consumed alcoholic drinks. All women agreed that including a prevention-focussed intervention would not reduce the likelihood of their attendance at screening mammograms or breast clinics. Qualitative data highlighted concerns in both women and staff of how to talk about alcohol and risk factors for breast cancer in a non-stigmatising way, as well as ambivalence from specialist staff as to their role in health promotion.ConclusionsLevels of alcohol health literacy and numeracy were low. Adding prevention interventions to screening and/or symptomatic clinics appears acceptable to attendees, highlighting the potential for using these opportunities as ‘teachable moments’. However, there are substantial cultural and systemic challenges to overcome if this is to be implemented successfully.

scholarly journals Integrating Somatic Mutations for Breast Cancer Survival Prediction Using Machine Learning Methods

2021 ◽  
Vol 11 ◽  
Author(s):  
Zongzhen He ◽  
Junying Zhang ◽  
Xiguo Yuan ◽  
Yuanyuan Zhang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Feature Selection ◽  
Somatic Mutations ◽  
Cancer Survival ◽  
Feature Selection Method ◽  
Breast Cancer Survival ◽  
Survival Prediction ◽  
Omics Data ◽  
Selection Methods

Breast cancer is the most common malignancy in women, and because it has a high mortality rate, it is urgent to develop computational methods to increase the accuracy of breast cancer survival predictive models. Although multi-omics data such as gene expression have been extensively used in recent studies, the accurate prognosis of breast cancer remains a challenge. Somatic mutations are another important and promising data source for studying cancer development, and its effect on the prognosis of breast cancer remains to be further explored. Meanwhile, these omics datasets are high-dimensional and redundant. Therefore, we adopted multiple kernel learning (MKL) to efficiently integrate somatic mutation to currently molecular data including gene expression, copy number variation (CNV), methylation, and protein expression data for the prediction of breast cancer survival. Before integration, the maximum relevance minimum redundancy (mRMR) feature selection method was utilized to select features that present high relevance to survival and low redundancy among themselves for each type of data. The experimental results demonstrated that the proposed method achieved the most optimal performance and there was a remarkable improvement in the prediction performance when somatic mutations were included, indicating that somatic mutations are critical for improving breast cancer survival predictions. Moreover, mRMR was superior to other feature selection methods used in previous studies. Furthermore, MKL outperformed the other traditional classifiers in multi-omics data integration. Our analysis indicated that through employing promising omics data such as somatic mutations and harnessing the power of proper feature selection methods and effective integration frameworks, the breast cancer survival predictive accuracy can be further increased, thereby providing a more optimal clinical diagnosis and more effective treatment for breast cancer patients.

Breast Cancer Risk factors in Relation to Molecular Subtypes in Breast Cancer Patients from Kenya.

2021 ◽  
Author(s):  
Shahin Sayed ◽  
Shaoqi Fan ◽  
Zahir Moloo ◽  
Ronald Wasike ◽  
Peter Bird ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Factor ◽  
Molecular Subtypes ◽  
Breastfeeding Duration ◽  
European Ancestry ◽  
Luminal A ◽  
Luminal B ◽  
Younger Age ◽  
Er Status

Abstract BackgroundFew studies have investigated risk factor heterogeneity by molecular subtypes in indigenous African populations where prevalence of traditional breast cancer (BC) risk factors, genetic background, and environmental exposures show marked differences compared to European ancestry populations. MethodsWe conducted a case-only analysis of 838 pathologically confirmed BC cases recruited from 5 groups of public, faith-based and private institutions across Kenya between March 2012 to May 2015. Centralized pathology review and immunohistochemistry (IHC) for key markers (ER, PR, HER2, EGFR, CK5-6, and Ki67) was performed to define subtypes. Risk factor data was collected at time of diagnosis through a questionnaire. Multivariable polytomous logistic regression models were used to determine associations between BC risk factors and tumor molecular subtypes, adjusted for clinical characteristics and risk factors.ResultsThe median age at menarche and first pregnancy were 14 and 21 years, median number of children was 3 and breastfeeding duration was 62 months per child. Distribution of molecular subtypes for luminal A, luminal B, HER2-enriched, and Triple Negative (TN) breast cancers was 34.8%, 35.8%, 10.7%, and 18.6%, respectively. After adjusting for covariates, compared to patients with ER positive tumors, ER negative patients were more likely to have higher parity (OR=2.03, 95% CI= (1.11, 3.72), p=0.021, comparing ≥5 to <2 children) and younger age at first pregnancy (ORtrend=0.77, 95% CItrend=0.61, 0.98, Ptrend=0.032, comparing older to younger age). Compared to patients with luminal A tumors, luminal B patients were more likely to have lower parity (OR=0.45, 95% CI= 0.23, 0.87, p=0.018, comparing ≥5 to <2 children); HER2-enriched patients were less likely to be obese (OR=0.36, 95% CI=0.16, 0.81, p=0.013) or older age at menopause (OR=0.38, 95% CI=0.15, 0.997, p=0.049). Body mass index (BMI), either overall or by menopausal status, did not vary significantly by ER status. Overall, cumulative or average breastfeeding duration did not vary significantly across subtypes. Conclusions In Kenya, we found associations between parity-related risk factors and ER status consistent with observations in European ancestry populations, but differing associations with BMI and breastfeeding. Inclusion of diverse populations in cancer etiology studies are needed to develop population and subtype specific risk prediction/prevention strategies.

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