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)

BackgroundBreast cancer is a leading global health challenge, exhibiting significant regional disparities in incidence, mortality, and survival outcomes. This study analyzed the burden of breast cancer in 2022 and projects its future impact by 2050 using GLOBOCAN data.MethodsIncidence and mortality data for breast cancer from 2022 were analyzed across continents, age group, HDI and countries categories. The Average Annual Percent Change (AAPC) from 2018 to 2022 was calculated to project cases and deaths for 2050. Mortality-to-Incidence Ratios (MIR) were computed to assess survival disparities.ResultsIn 2022, Asia accounted for the highest breast cancer incidence (985,817 cases), followed by Europe (557,532) and Northern America (306,307). Africa recorded the highest mortality-to-incidence ratio (MIR) of 0.510, highlighting challenges in early detection and treatment. By 2050, global breast cancer cases are projected to exceed 6 million, with Asia, experiencing the most significant rise (2.0 million cases) followed by Africa (1.118 million cases), followed by. Mortality is expected to rise proportionally, with Asia (484,468) and Africa (390,695 deaths) and bearing the largest burden. The MIR for 2050 shows marked disparities, with Africa (0.35) and Asia (0.25) remaining elevated compared to Europe (0.20) and Northern America (0.13).ConclusionThe projected rise in breast cancer incidence and mortality highlights the urgent need for region-specific interventions. Targeted strategies focusing on early detection, improved access to treatment, and reduction of modifiable risk factors are essential, particularly in transitioning economies where disparities remain stark.

Global Breast Cancer Screening (Programme Data) by Country, 2023 Discover more data with ReportLinker!

In 2018, the incidence rate for breast cancer among postmenopausal women in Western Europe was around *** per 100,000 population. This statistic shows the incidence rates for females with premenopausal and postmenopausal breast cancer worldwide in 2018 by region.

BACKGROUND Comprehensive analyses of statistical data on breast cancer incidence, mortality, and associated risk factors are of great value for decision-making related to reducing the disease burden of breast cancer. METHODS: Based on data from the Annual Report of China Tumour Registry and the Global Burden of Disease (GBD), we conducted summary and trend analyses of incidence and mortality rates of breast cancer in Chinese women from 2014 to 2018 for urban and rural areas in the whole, eastern, central, and western parts of the country, and projected the incidence and mortality rates of breast cancer for 2019 in comparison with the GBD 2019 estimates. And the comparative risk assessment framework estimated risk factors contributing to breast cancer deaths and disability-adjusted life years (DALYs) from GBD. RESULTS: The Annual Report of the Chinese Tumour Registry showed that showed that the mortality rate of breast cancer declined and the incidence rate remained largely unchanged from 2014 to 2018. There was a significant increasing trend in incidence rates among urban and rural women in eastern China and rural women in central China, whereas there was a significant decreasing trend in mortality rates among rural women in China. The two data sources have some differences in their predictions of breast cancer in China in 2019. The GBD data estimated the age-standard DALYs rates of high body-mass index, high fasting plasma glucose and diet high in red meat, which are the top three risk factors attributable to breast cancer in Chinese women, to be 29.99/100,000, 13.66/100,000 and 13.44/100,000, respectively. Conclusion: The trend of breast cancer incidence and mortality rates shown in the Annual Report of China Tumour Registry indicates that China has achieved remarkable results in reducing the burden of breast cancer, but there is still a need to further improve breast cancer screening and early diagnosis and treatment, and to improve the system of primary prevention. The GBD database provides risk factors for breast cancer in the world, Asia, and China, and lays the foundation for research on effective measures to reduce the burden of breast cancer.

Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen (on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and, if so, whether it has spread to other parts of the body.

This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.

The file contains separate sheets that provide pertinent metadata for assessing the incidence and mortality rates of breast cancer based on various factors such as time, gender, region, country, and socio-demographic index (SDI). In addition to this information, the document also includes data on the World population age standard, the HDI of different countries in 1990, and Global Population Forecasts spanning from 2017 to 2100.

Breast cancer is the most frequently diagnosed cancer and the most frequent cause for cancer-related deaths in women worldwide. Globally, breast cancer accounted for 2.08 million out of 18.08 million new cancer cases (incidence rate of 11.6%) and 626,679 out of 9.55 million cancer-related deaths (6.6% of all cancer-related deaths) in 2018. 1,2 In India, breast cancer has surpassed cancers of the cervix and the oral cavity to be the most common cancer and the leading cause of cancer deaths. In 2018, 159,500 new cases of breast cancer were diagnosed, representing 27.7% of all new cancers among Indian women and 11.1% of all cancer deaths.

In india breast cancer cases reporting and diagnotics have increased 10 times in past 3 years . All thanks to the various cancer awareness initiatives by both private and govt. organisations.

Background: Data on burden and changing trends of breast cancer are of value for policymaking. We aimed to determine the pattern of breast cancer incidence, mortality, and disability-adjusted life-years (DALYs), as well as temporal trends, from 1990 to 2017.Methods: We collected detailed information on breast cancer between 1990 and 2017 using the results of the Global Burden of Disease study. The number of incident cases, deaths, and DALYs attributable to breast cancer are reported as well as age-standardized rates. Estimated annual percentage changes (EAPCs) in age-standardized rates were calculated to quantify the temporal trends. Moreover, the attributable burden to breast cancer risk factors was also estimated.Results: There were 1,960,682 incident cases and 611,625 deaths of breast cancer globally in 2017, contributing to 17,708,600 DALYs. The age-standardized incidence rates (ASIRs) increased between 1990 and 2017, while the age-standardized mortality rates and DALY rates decreased. The corresponding EAPCs were 0.41, −0.62, and −0.56, respectively. These trends were heterogeneous across regions and countries. The increase in the ASIRs was more prominent in countries with a low sociodemographic index. The percentages of breast cancer deaths due to alcohol use and tobacco were decreasing, while deaths due to high body mass index and high fasting plasma glucose were increasing.Conclusion: Breast cancer remained a major public health concern globally. The trends of incidence, mortality, and DALYs were heterogeneous across regions and countries, suggesting that the allocation of appropriate health care resources for breast cancer should be considered at the national scale and even at the subnational scale.

In 2020, Melanesia was the region with the highest death rate due to breast cancer worldwide, with around 27.5 deaths per 100,000 women. This statistic shows the age-standardized mortality rate (deaths) of breast cancer among women worldwide, by region.

BackgroundThe burden of breast cancer has been increasing globally. The epidemiology burden and trends need to be updated. This study aimed to update the burden and trends of breast cancer incidences, deaths, and disability-adjusted life-years (DALYs) from 1990 to 2019, using the Global Burden of Disease 2019 study.MethodsThe data of incidences, deaths, DALYs, and age-standardized rates were extracted. Estimated annual percentage changes were used to quantify the trends of age-standardized rates. Besides, the population attributable fractions of the risk factors of breast cancer were also estimated.ResultsGlobally, the incidences of breast cancer increased to 2,002,354 in 2019. High social-development index (SDI) quintiles had the highest incidence cases with a declining trend in age-standardized incidence rate. In 2019, the global deaths and DALYs of breast cancer increased to 700,660 and 20,625,313, respectively. From 1990 to 2019, the age-standardized mortality rates and age-standardized DALY rates declined globally, especially in high and high-middle SDI quintiles. Besides, the trends varied from different regions and countries. The proportion of the patients in the 70+ years age group increased globally. Deaths of breast cancer attributable to high fasting plasma glucose and high body mass index increased globally, and high fasting plasma glucose was the greatest contributor to the global breast cancer deaths.ConclusionThe burden of breast cancer in higher SDI quintiles had gone down while the burden was still on the rise in lower SDI quintiles. It is necessary to appeal to the public to decrease the exposure of the risk factors.

📄 Dataset Description: This dataset contains global cancer patient data reported from 2015 to 2024, designed to simulate the key factors influencing cancer diagnosis, treatment, and survival. It includes a variety of features that are commonly studied in the medical field, such as age, gender, cancer type, environmental factors, and lifestyle behaviors. The dataset is perfect for:

Exploratory Data Analysis (EDA)

Multiple Linear Regression and other modeling tasks

Feature Selection and Correlation Analysis

Predictive Modeling for cancer severity, treatment cost, and survival prediction

Data Visualization and creating insightful graphs

Key Features: Age: Patient's age (20-90 years)

Gender: Male, Female, or Other

Country/Region: Country or region of the patient

Cancer Type: Various types of cancer (e.g., Breast, Lung, Colon)

Cancer Stage: Stage 0 to Stage IV

Risk Factors: Includes genetic risk, air pollution, alcohol use, smoking, obesity, etc.

Treatment Cost: Estimated cost of cancer treatment (in USD)

Survival Years: Years survived since diagnosis

Severity Score: A composite score representing cancer severity

This dataset provides a broad view of global cancer trends, making it an ideal resource for those learning data science, machine learning, and statistical analysis in healthcare.

This dataset is taken from the UCI Machine Learning Repository (Link: https://data.world/health/breast-cancer-wisconsin) by the Donor: Nick Street

The main idea and inspiration behind the upload was to provide datasets for Machine Learning as practice and reference for my peers at college. The main purpose is to analyze data and experiment with different machine learning ideas and techniques for this binary classification task. As such, this dataset is a very useful resource to practice on.

Breast cancer is when breast cells mutate and become cancerous cells that multiply and form tumors. It accounts for 25% of all cancer cases and affected over 2.1 Million people in 2015 alone. Breast cancer typically affects women and people assigned female at birth (AFAB) age 50 and older, but it can also affect men and people assigned male at birth (AMAB), as well as younger women. Healthcare providers may treat breast cancer with surgery to remove tumors or treatment to kill cancerous cells.

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at http://www.cs.wisc.edu/~street/images/

The task: To classify whether the tumor is benign (B) or malignant (M).

Relevant information

Features are computed from a digitized image of a fine needle
aspirate (FNA) of a breast mass. They describe
characteristics of the cell nuclei present in the image.
A few of the images can be found at
http://www.cs.wisc.edu/~street/images/

Separating plane described above was obtained using
Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
Construction Via Linear Programming." Proceedings of the 4th
Midwest Artificial Intelligence and Cognitive Science Society,
pp. 97-101, 1992], a classification method which uses linear
programming to construct a decision tree. Relevant features
were selected using an exhaustive search in the space of 1-4
features and 1-3 separating planes.

The actual linear program used to obtain the separating plane
in the 3-dimensional space is that described in:
[K. P. Bennett and O. L. Mangasarian: "Robust Linear
Programming Discrimination of Two Linearly Inseparable Sets",
Optimization Methods and Software 1, 1992, 23-34].


This database is also available through the UW CS ftp server:

ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/

Number of instances: 569

Number of attributes: 32 (ID, diagnosis, 30 real-valued input features)

Original Creators:

Dr. William H. Wolberg, General Surgery Dept., University of
Wisconsin, Clinical Sciences Center, Madison, WI 53792
wolberg@eagle.surgery.wisc.edu

W. Nick Street, Computer Sciences Dept., University of
Wisconsin, 1210 West Dayton St., Madison, WI 53706
street@cs.wisc.edu 608-262-6619

Olvi L. Mangasarian, Computer Sciences Dept., University of
Wisconsin, 1210 West Dayton St., Madison, WI 53706
olvi@cs.wisc.edu 

Donor: Nick Street

Date: November 1995

Past Usage:

first usage:

W.N. Street, W.H. Wolberg and O.L. Mangasarian 
Nuclear feature extraction for breast tumor diagnosis.
IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science
and Technology, volume 1905, pages 861-870, San Jose, CA, 1993.

OR literature:

O.L. Mangasarian, W.N. Street and W.H. Wolberg. 
Breast cancer diagnosis and prognosis via linear programming. 
Operations Research, 43(4), pages 570-577, July-August 1995.

Medical literature:

W.H. Wolberg, W.N. Street, and O.L. Mangasarian. 
Machine learning techniques to diagnose breast cancer from
fine-needle aspirates. 
Cancer Letters 77 (1994) 163-171.

W.H. Wolberg, W.N. Street, and O.L. Mangasarian. 
Image analysis and machine learning applied to breast cancer
diagnosis and prognosis. 
Analytical and Quantitative Cytology and Histology, Vol. 17
No. 2, pages 77-87, April 1995. 

W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. 
Computerized breast cancer diagnosis and prognosis from fine
needle aspirates. 
Archives of Surgery 1995;130:511-516.

W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. 
Computer-derived nuclear features distinguish malignant from
benign breast cytology. 
Human Pathology, 26:792--796, 1995.

See also: http://www.cs.wisc.edu/~olvi/uwmp/mpml.html http://www.cs.wisc.edu/~olvi/uwmp/cancer.html

In 2020, Eastern Asia accounted for around 21 percent of deaths due to breast cancer worldwide. This statistic shows the distribution of breast cancer deaths among women worldwide, by region.

Number and rate of new cancer cases diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

What is Breast Cancer Dataset?

Breast cancer is the most common cancer amongst women in the world. It accounts for 25% of all cancer cases and affected over 2.1 Million people in 2015 alone. It starts when cells in the breast begin to grow out of control. These cells usually form tumors that can be seen via X-ray or felt as lumps in the breast area.

.

https://user-images.githubusercontent.com/36210723/182301443-382b14e1-71c1-46ac-88f5-e72a9b2083e7.jpg" alt="cancer-1">

.

How to use this dataset

The key challenge against its detection is how to classify tumors into malignant (cancerous) or benign(non-cancerous). We ask you to complete the analysis of classifying these tumors using machine learning (with SVMs) and the Breast Cancer Wisconsin (Diagnostic) Dataset.

Acknowledgments

When we use this dataset in our research, we credit the authors as :

• License : CC BY 4.0.

• This data set is taken from https://data.world/health/breast-cancer-wisconsin by the Donor: Nick Street and the Source: UCI - Machine Learning Repository.

The main idea for uploading this dataset is to practice data analysis with my students, as I am working in college and want my student to train our studying ideas in a big dataset, It may be not up to date and I mention the collecting years, but it is a good resource of data to practice

In 2022, the highest cancer rate for men and women among European countries was in Denmark with 728.5 cancer cases per 100,000 population. Ireland and the Netherlands followed, with 641.6 and 641.4 people diagnosed with cancer per 100,000 population, respectively.
Lung cancer Lung cancer is the deadliest type of cancer worldwide, and in Europe, Germany was the country with the highest number of lung cancer deaths in 2022, with 47.7 thousand deaths. However, when looking at the incidence rate of lung cancer, Hungary had the highest for both males and females, with 138.4 and 72.3 cases per 100,000 population, respectively.
Breast cancer Breast cancer is the most common type of cancer among women with an incidence rate of 83.3 cases per 100,000 population in Europe in 2022. Cyprus was the country with the highest incidence of breast cancer, followed by Belgium and France. The mortality rate due to breast cancer was 34.8 deaths per 100,000 population across Europe, and Cyprus was again the country with the highest figure.

Breast Cancer Diagnostic Market Outlook

The global breast cancer diagnostic market size was valued at approximately USD 4.5 billion in 2023 and is projected to reach USD 9.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.5% during the forecast period. The rising prevalence of breast cancer, coupled with advances in diagnostic technologies, significantly drives the market growth. Other factors include increased awareness about early detection and the availability of substantial government funding for cancer research and diagnostics.

One of the primary growth factors in the breast cancer diagnostic market is the increasing incidence of breast cancer globally. According to the World Health Organization (WHO), breast cancer is the most common cancer among women worldwide, leading to significant morbidity and mortality. The rising awareness about the importance of early diagnosis, which significantly improves survival rates, has encouraged more women to undergo regular screenings. This has, in turn, driven the demand for advanced diagnostic tools and technologies.

Technological advancements in diagnostic tools are another critical growth driver for the breast cancer diagnostic market. Innovations such as digital mammography, 3D imaging, and artificial intelligence (AI)-driven diagnostic solutions have revolutionized the early detection and accurate diagnosis of breast cancer. These technologies offer enhanced image clarity, reduced false positives, and faster results, leading to better patient outcomes. Continuous research and development in this area are expected to bring more sophisticated and effective diagnostic solutions to the market.

The increasing availability of government and private funding for breast cancer research and diagnostic initiatives also propels market growth. Many countries have implemented screening programs and awareness campaigns to educate women about the importance of early detection. Additionally, several non-profit organizations and advocacy groups are actively involved in promoting breast cancer awareness and funding research projects, further boosting the demand for diagnostic services.

From a regional perspective, North America dominates the breast cancer diagnostic market, followed by Europe, due to the high prevalence of breast cancer, well-established healthcare infrastructure, and significant investment in research and development. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing awareness, improving healthcare facilities, and rising healthcare expenditure in countries such as China and India.

Test Type Analysis

The breast cancer diagnostic market can be segmented by test type into mammography, biopsy, ultrasound, MRI, CT scan, PET scan, and others. Mammography is the most commonly used test for breast cancer screening and diagnosis. Digital mammography, which offers superior image quality and lower radiation exposure compared to traditional film mammography, has gained significant traction. The advent of 3D mammography or tomosynthesis has further enhanced the accuracy of breast cancer detection, reducing false positives and improving patient outcomes.

Biopsy remains the gold standard for definitive diagnosis of breast cancer. Various biopsy techniques such as fine-needle aspiration, core needle biopsy, and surgical biopsy are employed based on the clinical scenario. The integration of imaging technologies with biopsy procedures, such as stereotactic and ultrasound-guided biopsies, has improved the precision and accuracy of tissue sampling, thereby enhancing diagnostic outcomes. The development of minimally invasive biopsy techniques has also reduced patient discomfort and recovery time.

Ultrasound is commonly used as an adjunct to mammography to evaluate breast abnormalities, particularly in women with dense breast tissue. The non-invasive nature of ultrasound, combined with its ability to differentiate between cystic and solid masses, makes it a valuable tool in breast cancer diagnosis. Advances in ultrasound technology, such as the development of elastography, have further improved its diagnostic accuracy by assessing tissue stiffness, which is indicative of malignancy.

MRI and PET scans are primarily used for staging breast cancer and evaluating the extent of disease spread. MRI offers excellent soft tissue contrast and is particularly useful in assessing the involvement of the chest wall and lymph nodes. PET scans, on the other hand, provide functional

Number and rate of new cancer cases by stage at diagnosis from 2011 to the most recent diagnosis year available. Included are colorectal, lung, breast, cervical and prostate cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

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.