Editor’s Choice

• Global Breast Cancer Market size is expected to be worth around USD 49.2 Bn by 2032 from USD 19.8 Bn in 2022, growing at a CAGR of 9.8% during the forecast period from 2022 to 2032.
• Breast cancer is the most common cancer among women worldwide. In 2020, there were about 2.3 million new cases of breast cancer diagnosed globally.
• Breast cancer is the leading cause of cancer-related deaths in women. In 2020, it was responsible for approximately 685,000 deaths worldwide.
• The survival rate of breast cancer has improved over the years. In the United States, the overall five-year survival rate of breast cancer is around 90%.
• The American Cancer Society recommends annual mammograms starting at age 40 for women at average risk.
• Although rare, breast cancer also occurs in men. Less than 1% of breast cancer cases are diagnosed in males.

(Source: WHO, American Cancer Society)

Mortality from breast cancer (ICD-10 C50 equivalent to ICD-9 174). To reduce deaths from breast cancer. Legacy unique identifier: P00159

Relationships that failed to meet the proportional hazards assumption denoted in bold and by **. All others (non-bold) provided for completeness.

IntroductionLung and breast cancer are important in the working-age population both in terms of incidence and costs. The study aims were to estimate the 10-year risk of lung and breast cancer by occupation and smoking status and to create easy to use age-, and sex-specific 10-year risk charts.MethodsNew lung and breast cancer cases between 2010 and 2014 from all 5 cancer registries of Western Switzerland, matched with the Swiss National Cohort were used. The 10-year risks of lung and breast cancer by occupational category were estimated. For lung cancer, estimates were additionally stratified by smoking status using data on smoking prevalence from the 2007 Swiss Health Survey.ResultsThe risks of lung and breast cancer increased with age and were the highest for current smokers. Men in elementary professions had a higher 10-year risk of developing lung cancer compared to men in intermediate and managerial professions. Women in intermediate professions had a higher 10-year risk of developing lung cancer compared to elementary and managerial professions. However, women in managerial professions had the highest risk of developing breast cancer.DiscussionThe 10-year risk of lung and breast cancer differs substantially between occupational categories. Smoking creates greater changes in 10-year risk than occupation for both sexes. The 10-year risk is interesting for both patients and professionals to inform choices related to cancer risk, such as screening and health behaviors. The risk charts can also be used as public health indicators and to inform policies to protect workers.

BackgroundCardiovascular disease (CVD) is the leading cause of morbidity and mortality among older postmenopausal women. The impact of postmenopausal breast cancer on CVD for older women is uncertain. We hypothesized that older postmenopausal women with breast cancer would be at a higher risk of CVD than similar aged women without breast cancer and that CVD would be a major contributor to the subsequent morbidity and mortality.MethodsIn a prospective Women’s Health Initiative study, incident CVD events and total and cause-specific death rates were compared between postmenopausal women with (n = 4,340) and without (n = 97,576) incident invasive breast cancer over 10 years post-diagnosis, stratified by 3 age groups (50–59, 60–69, and 70–79).ResultsPostmenopausal women, regardless of breast cancer diagnosis, had similar and high levels of CVD risk factors (e.g., smoking and hypertension) at baseline prior to breast cancer, which were strong predictors of CVD and total mortality over time. CVD affected mostly women age 70–79 with localized breast cancer (79% of breast cancer cases in 70–79 age group): only 17% died from breast cancer and CVD was the leading cause of death (22%) over the average 10 years follow up. Compared to age-matched women without breast cancer, women age 70–79 at diagnosis of localized breast cancer had a similar multivariate-adjusted hazard ratio (HR) of 1.01 (95% confidence interval [CI]: 0.76–1.33) for coronary heart disease, a lower risk of composite CVD (HR = 0.84, 95% CI: 0.70–1.00), and a higher risk of total mortality (HR = 1.20, 95% CI: 1.04–1.39).ConclusionCVD was a major contributor to mortality in women with localized breast cancer at age 70–79. Further studies are needed to evaluate both screening and treatment of localized breast cancer tailored to the specific health issues of older women.

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes.Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction.Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data.Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data.

ObjectivesBreast cancer is the leading cause of death in women around the world. Its occurrence and development have been linked to genetic factors, living habits, health conditions, and socioeconomic factors. Comparisons of incidence and mortality rates of female breast cancer are useful approaches to define cancer-related socioeconomic disparities.MethodsThis was a retrospective observational cohort study on breast cancer of women in several developed countries over 30 years. Effects of socioeconomic factors were analyzed using a path diagram method.ResultsWe found a positive, significant association of public wealth on incidence and mortality of breast cancer, and the path coefficients in the structural equations are −0.51 and −0.39, respectively. The unemployment rate (UR) is critical and the path coefficients are all 0.2. The path coefficients of individual economic wealth to the rates of breast cancer are 0.18 and 0.27, respectively.ConclusionThe influence of social pressure on the incidence and mortality of breast cancer was not typical monotonous. The survival rate of breast cancer determined by the ratio of mortality rate to incidence rate showed a similar pattern with socioeconomic factors.

BackgroundThis nationwide study examined breast cancer (BC) incidence and mortality rates in Hungary between 2011–2019, and the impact of the Covid-19 pandemic on the incidence and mortality rates in 2020 using the databases of the National Health Insurance Fund (NHIF) and Central Statistical Office (CSO) of Hungary.MethodsOur nationwide, retrospective study included patients who were newly diagnosed with breast cancer (International Codes of Diseases ICD)-10 C50) between Jan 1, 2011 and Dec 31, 2020. Age-standardized incidence and mortality rates (ASRs) were calculated using European Standard Populations (ESP).Results7,729 to 8,233 new breast cancer cases were recorded in the NHIF database annually, and 3,550 to 4,909 all-cause deaths occurred within BC population per year during 2011-2019 period, while 2,096 to 2,223 breast cancer cause-specific death was recorded (CSO). Age-standardized incidence rates varied between 116.73 and 106.16/100,000 PYs, showing a mean annual change of -0.7% (95% CI: -1.21%–0.16%) and a total change of -5.41% (95% CI: -9.24 to -1.32). Age-standardized mortality rates varied between 26.65–24.97/100,000 PYs (mean annual change: -0.58%; 95% CI: -1.31–0.27%; p=0.101; total change: -5.98%; 95% CI: -13.36–2.66). Age-specific incidence rates significantly decreased between 2011 and 2019 in women aged 50–59, 60–69, 80–89, and ≥90 years (-8.22%, -14.28%, -9.14%, and -36.22%, respectively), while it increased in young females by 30.02% (95%CI 17,01%- 51,97%) during the same period. From 2019 to 2020 (in first COVID-19 pandemic year), breast cancer incidence nominally decreased by 12% (incidence rate ratio [RR]: 0.88; 95% CI: 0.69–1.13; 2020 vs. 2019), all-cause mortality nominally increased by 6% (RR: 1.06; 95% CI: 0.79–1.43) among breast cancer patients, and cause-specific mortality did not change (RR: 1.00; 95%CI: 0.86–1.15).ConclusionThe incidence of breast cancer significantly decreased in older age groups (≥50 years), oppositely increased among young females between 2011 and 2019, while cause-specific mortality in breast cancer patients showed a non-significant decrease. In 2020, the Covid-19 pandemic resulted in a nominal, but not statistically significant, 12% decrease in breast cancer incidence, with no significant increase in cause-specific breast cancer mortality observed during 2020.

Get ready for an exciting adventure into the world of machine-learning models on Kaggle! Our dataset is like a puzzle waiting to be solved. We've designed it carefully, and it's all about Breast Cancer data. Imagine exploring a treasure trove of numbers that reveal how different models perform. See the magic of advanced methods and colorful graphs that show accuracy, precision, recall, and F1-score. This dataset isn't just numbers – it's an opportunity to challenge yourself, find hidden patterns, and prove your data skills. We've made it just for you, so you can uncover the secrets of machine learning and shine on Kaggle!

The Column Description includes,

1. Model Name: The name of the machine learning model used for prediction.
2. Hyperparameters: The configuration settings used for the model, showcase the versatility of model tuning.
3. Accuracy: The proportion of correctly predicted instances out of the total instances, indicating overall model performance.
4. Precision: The ratio of correctly predicted positive instances to all instances predicted as positive, reflecting model's accuracy in positive predictions.
5. Recall: The ratio of correctly predicted positive instances to all actual positive instances, measuring model's ability to capture positive cases.
6. F1-Score: The harmonic mean of precision and recall, providing a balanced assessment of model performance.
7. Classification Report: A comprehensive summary of precision, recall, F1-score, and support for both classes (0 and 1), offering insights into class-specific performance.
8. FPR (False Positive Rate): The ratio of incorrectly predicted negative instances to all actual negative instances, revealing the cost of false positives.
9. TPR (True Positive Rate): Synonymous with recall, indicating the model's ability to identify positive instances.
10. ROC AUC: The Area Under the Receiver Operating Characteristic curve, illustrating the trade-off between true positive rate and false positive rate.
11. Precision-Recall Curve: A graphical representation of precision and recall values across different thresholds, aiding in model selection based on specific requirements.
12. PR AUC (Precision-Recall AUC): The Area Under the Precision-Recall curve, a valuable metric for imbalanced datasets.

🦠 Breast Cancer Data Set

This dataset contains the characteristics of patients diagnosed with cancer. The dataset contains a unique ID for each patient, the type of cancer (diagnosis), the visual characteristics of the cancer and the average values of these characteristics.

📚 The main features of the dataset are as follows:

1. id: Represents a unique ID of each patient.
2. diagnosis: Indicates the type of cancer. This property can take the values "M" (Malignant - Benign) or "B" (Benign - Malignant).
3. radius_mean, texture_mean, perimeter_mean, area_mean, smoothness_mean, compactness_mean, concavity_mean, concave points_mean: Represents the mean values of the cancer's visual characteristics.

There are also several categorical features where patients in the dataset are labeled with numerical values. You can examine them in the Chart area.

Other features contain specific ranges of average values of the features of the cancer image:

• radius_mean, texture_mean, perimeter_mean, area_mean, smoothness_mean, compactness_mean, concavity_mean, concave points_mean

Each of these features is mapped to a table containing the number of values in a given range. You can examine the Chart Tables

Each sample contains the patient's unique ID, the cancer diagnosis and the average values of the cancer's visual characteristics.

Such a dataset can be used to train or test models and algorithms used to make cancer diagnoses. Understanding and analyzing the dataset can contribute to the improvement of cancer-related visual features and diagnosis.

✨ Examples of Projects that can be done with the Data Set

Logistic Regression: This algorithm can be used effectively for binary classification problems. In this dataset, logistic regression may be an appropriate choice since there are "Malignant" (benign) and "Benign" (malignant) classes. It can be used to predict cancer type with the visual features in the dataset.

K-Nearest Neighbors (KNN): KNN classifies an example by looking at the k closest examples around it. This algorithm assumes that patients with similar characteristics tend to have similar types of cancer. KNN can be used for cancer diagnosis by taking into account neighborhood relationships in the data set.

Support Vector Machines (SVM): SVM is effective for classification tasks, especially for two-class problems. Focusing on the clear separation of classes in the dataset, SVM is a powerful algorithm that can be used for cancer diagnosis.

Data Set Related Training Notebooks 😊 ("I Recommend You Review")

K-NN Project: https://www.kaggle.com/code/erdemtaha/prediction-cancer-data-with-k-nn-95

Logistic Regressüon: https://www.kaggle.com/code/erdemtaha/cancer-prediction-96-5-with-logistic-regression

💖 Acknowledgements and Information

This is a copy of content that has been elaborated for educational purposes and published to reach more people, you can access the original source from the link below, please do not forget to support that data

🔗 https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data

This database can also be accessed via the UW CS ftp server: 🔗 ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/

It can also be found at the UCI Machine Learning Repository: 🔗 https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

📩 Personal Information:

If you have some questions or curiosities about the data or studies, you can contact me as you wish from the links below 😊

LinkedIn: https://www.linkedin.com/in/erdem-taha-sokullu/

Mail: erdemtahasokullu@gmail.com

Github: https://github.com/Prometheussx

Kaggle: https://www.kaggle.com/erdemtaha

📜 License:

This Data has a CC BY-NC-SA 4.0 License You can review the license rules from the link below

License Link: https://creativecommons.org/licenses/by-nc-sa/4.0/

In 2022, female breast cancer was the type of alcohol-associated cancer with the highest incidence in the United States, with a rate of nearly 138 per 100,000 people. This graph shows the rate of alcohol-related cancers per 100,000 people in the United States in 2022, by cancer type.

Background The objective of the present study was to examine the prognostic and predictive significance of the apoptosis-related marker Fas ligand (FasL):Fas ratio in breast cancer. Methods Tumour biopsies from 215 primary invasive breast cancer patients were examined for the expression of FasL and Fas mRNA transcripts by quantitative real-time RT-PCR. Their prognostic and predictive impact on patient survival was determined in univariate and multivariate survival analyses. Results Using a cutoff value of 1, a FasL:Fas ratio greater than 1 was found to have significant prognostic value for disease-free survival among the total population (median follow up 54 months). It was associated with a significantly decreased disease-free survival (P = 0.022) and with a tendency toward increased mortality (P = 0.14) in univariate analysis. Hormone receptor positive women exclusively treated with tamoxifen (n = 86) and with a FasL:Fas ratio greater than 1 had a significantly decreased disease-free survival (P = 0.008) and overall survival (P = 0.03) in univariate Kaplan–Meier analysis. Furthermore, tumour size and FasL:Fas ratio were of independent predictive significance in the multivariate model for disease-free and overall survival in that subgroup. Among postmenopausal patients (n = 148) both of those factors retained independent prognostic significance in the multivariate model for disease-free survival. In contrast, FasL:Fas ratio had no significant predictive value in patients exclusively treated with chemotherapy. Conclusion The data presented indicate that FasL:Fas ratio may be useful not only as a prognostic factor but also as a predictive factor for projecting response to the antioestrogen tamoxifen. The results strongly support a correlation between FasL:Fas ratio greater than 1 and lack of efficacy of tamoxifen in hormone receptor positive patients.

Cardiovascular disease (CVD) and death in women with localized breast cancer a vs. women with no breast cancera.

Table 1. Samples analyzed by NGS Table 2. VHIO-300 results from solid tumor Table 3. VHIO-YWBC results from BM positive for ctDNA

Supplementary Tables S1-S15

Breast cancer is a worldwide threat to female health with patient outcomes varying widely. The exact correlation between global outcomes of breast cancer and the national socioeconomic status is still undetermined. Mortality-to-incidence ratio (MIR) of breast cancer was calculated with the contemporary age standardized incidence and mortality rates for countries with data available at GLOBOCAN 2012 database. The MIR matched national human development indexes (HDIs) and health system attainments were respectively obtained from Human Development Report and World Health Report. Correlation analysis, regression analysis, and Tukey-Kramer post hoc test were used to explore the effects of HDI and health system attainment on breast cancer MIR. Our results demonstrated that breast cancer MIR was inversely correlated with national HDI (r = -.950; P < .001) and health system attainment (r = -.898; P < .001). Countries with very high HDI had significantly lower MIRs than those with high, medium and low HDI (P < .001). Liner regression model by ordinary least squares also indicated negative effects of both HDI (adjusted R2 = .903, standardize β = -.699, P < .001) and health system attainment (adjusted R2 =. 805, standardized β = -.009; P < .001), with greater effects in developing countries identified by quantile regression analysis. It is noteworthy that significant health care disparities exist among countries in accordance with the discrepancy of HDI. Policies should be made in less developed countries, which are more likely to obtain worse outcomes in female breast cancer, that in order to improve their comprehensive economic strength and optimize their health system performance.

Associations between baseline risk factors and coronary heart disease (CHD) in women with breast cancer.

Table S6: Differentially expressed transcripts in PRPF8-KD cells;Table S7: Differentially expressed transcripts in PRPF38A-KD cells

Breast and cervical cancer exams in Brazil

Ratio between mammography exams in women aged 50 to 69 and female population of the same age group and place of residence

Ratio between cervical cytopathological exams in women aged 25 to 59 years and female population of the same age group and place of residence

Content

1. Data set with the relationship between the number of screening mammography exams performed and paid for by SUS (Unified Health System in Brazil), in women aged 50 to 69 years living in a municipality, in the period of two years, and the female population of the same age group, residing in the same municipality, in the last year of the biennium.

It allows to know the number of mammograms performed on women aged 50 to 69 years, making it possible to infer the inequalities in access to mammography and in the screening of breast cancer in women aged 50 to 69 years.

1. Data set with the relationship between the number of cervical cytopathological examinations, performed and paid for by SUS (Unified Health System in Brazil), in women aged 25 to 59 years residing in a municipality, over a three-year period, and the female population of the same age group, residing in the same municipality , in the last year of the triennium

Expresses the triennial performance of cervical cytopathological exams (Papanicolau) for the female population living in a municipality, from 25 to 59 years old, target for the screening of cervical cancer, indicating the access obtained or coverage performed for such procedure.

Data Source

• Author and maintainer of the data is: Sala de Apoio à Gestão Estratégica - SAGE
• All data are available from the Brazilian government and the Ministry of Health. Mammography data: dados.gov.br Cervical cytopathological data: dados.gov.br

Odds ratio (OR) and 95% confidence interval (95% CI) of breast cancer across quartiles of four dietary patterns by menopausal statusa.