The rate of breast cancer deaths in the U.S. has dramatically declined since 1950. As of 2023, the death rate from breast cancer was **** per 100,000 population. However, cancer is a serious public health issue in the United States and is the second leading cause of death among women. Breast cancer incidence Breast cancer symptoms include lumps or thickening of the breast tissue and may include changes to the skin. Breast cancer is driven by many factors, but age is a known risk factor. Among all age groups, the highest number of invasive breast cancer cases were among those aged 60 to 69. The incidence rate of new breast cancer cases is higher in some ethnicities than others. White, non-Hispanic women have the highest incidence rate of breast cancer, followed by non-Hispanic Black women. Breast cancer treatment Breast cancer treatments usually involve several methods, including surgery, chemotherapy and biological therapy. Types of cancer diagnosed at earlier stages often require fewer treatments. A majority of early stage breast cancer cases in the U.S. receive breast conserving surgery and radiation therapy.

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• 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)

In 2022, there were around *** new cases of breast cancer per 100,000 population in the state of Connecticut, making it the state with the highest breast cancer incidence rate that year. This statistic shows the incidence rate of breast cancer in the U.S. in 2022, by state.

In 2022, 175 females per 100,000 population were registered in England as newly diagnosed with breast cancer. This was an overall increase in comparison to the last few years' rate of registration. This statistic shows the rate of newly diagnosed female cases of breast cancer per 100,000 population in England from 1995 to 2022.

The dataset consists of the state wise estimated incidence of breast cancer and cervical cancer in India as per the National Cancer Registry Programme. The estimates are computer using age specific incidence Rate of 28 PBCRs of 2012-2016 and the projected population (person-years). NB: Incidence estimates of breast cancer is available since 2016 while that of cervical cancer is available since 2015.

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.

In 2023, there were **** deaths from breast cancer per 100,000 population in the state of South Dakota, the lowest of any state that year. This statistic shows the death rate from breast cancer in the U.S. in 2023, by state.

This dataset of breast cancer patients was obtained from the 2017 November update of the SEER Program of the NCI, which provides information on population-based cancer statistics. The dataset involved female patients with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3) diagnosed in 2006-2010. Patients with unknown tumour size, examined regional LNs, positive regional LNs, and patients whose survival months were less than 1 month were excluded; thus, 4024 patients were ultimately included.

What are Cancer Statistics in US States?

The circled group of good survivors has genetic indicators of poor survivors (i.e. low ESR1 levels, which is typically the prognostic indicator of poor outcomes in breast cancer) – understanding this group could be critical for helping improve mortality rates for this disease. Why this group survived was quickly analysed by using the Outcome Column (here Event Death - which is binary - 0,1) as a Data Lens (which we term Supervised vs Unsupervised analyses).

How to use this dataset

• A network was built using only gene expression with 272 breast cancer patients (as rows), and 1570 columns.

• Metadata includes patient info, treatment, and survival.

• Each node is a group of patients similar to each other. Flares (left) represent sub-populations that are distinct from the larger population. (One differentiating factor between the two flares is estrogen expression (low = top flare, high = bottom flare)).

• A bottom flare is a group of patients with 100% survival. The top flare shows a range of survival – very poor towards the tip (red), and very good near the base (circled).

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://query.data.world/s/yi422lv7mkhnydnt4ixrfujmoaglpk .

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

Rate: Number of new cases of breast cancer (per 100,000) diagnosed at the regional or distant stage among females.

Definition: Age-adjusted incidence rate of invasive breast cancer per 100,000 female population.

Data Sources:

(1) NJ State Cancer Registry, Dec 31, 2015 Analytic File, using NCI SEER*Stat ver 8.2.1 (www.seer.cancer.gov/seerstat)

(2) NJ population estimates as calculated by the NCI's SEER Program, released January 2015, http://www.seer.cancer.gov/popdata/download.html.

examined regional LNs

Death rate has been age-adjusted to the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Obesity can increase an individual’s lifetime risk of breast cancer. Promoting healthy food retail and physical activity and improving access to preventive care services are important measures that cities and communities can take to prevent breast cancer.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This dataset contains Cancer Incidence data for Breast Cancer (All Stages^) including: Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2016 to 2020.Data are for females segmented by age (All Ages, Ages Under 50, Ages 50 & Over, Ages Under 65, and Ages 65 & Over), with field names and aliases describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.govData NotationsState Cancer Registries may provide more current or more local data.TrendRising when 95% confidence interval of average annual percent change is above 0.Stable when 95% confidence interval of average annual percent change includes 0.Falling when 95% confidence interval of average annual percent change is below 0.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84, 85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used for SEER and NPCR incidence rates.‡ Incidence Trend data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information.Rates and trends are computed using different standards for malignancy. For more information see malignant.^ All Stages refers to any stage in the Surveillance, Epidemiology, and End Results (SEER) summary stage.Data Source Field Key(1) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(5) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(6) Source: National Program of Cancer Registries SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention (based on the 2022 submission).(7) Source: SEER November 2022 submission.(8) Source: Incidence data provided by the SEER Program. AAPCs are calculated by the Joinpoint Regression Program and are based on APCs. Data are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84,85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used with SEER November 2022 data.Some data are not available, see Data Not Available for combinations of geography, cancer site, age, and race/ethnicity.Data for the United States does not include data from Nevada.Data for the United States does not include Puerto Rico.

Mortality from breast cancer (ICD-10 C50 equivalent to ICD-9 174). To reduce deaths from breast cancer. Legacy unique identifier: P00148

This data package contains information on cancer its type, its occurrence by age, type and site. It also provides detailed data on adult and childhood cancer survival rates and deaths caused by breast cancer in females.

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.

Additional file 1. Supplementary Fig. 1. Graphical display of birth year, age distribution and follow up period for the three cohorts. Cohort 1 is marked in yellow, cohort 2 in orange and cohort 3 in blue. Age according to birth year and year of follow up is listed in the background. Supplementary Fig. 2. Incidence rates according to age, years of birth and proliferative marker status. Blue lines: women born before 1929. Red lines: Women born in 1929 or later. Dotted lines (red and blue) represent incidence rates of observed cases. Solid lines (red and blue) represent average incidence rates based on 50 imputed datasets with corresponding 95% CI

Description:

Explore the field of breast cancer diagnosis with the insightful Wisconsin Breast Cancer dataset (Original). This dataset provides detailed attributes representing tumor characteristics observed in breast tissue samples. By analyzing these attributes, researchers and medical professionals can gain insights into tumor behavior and develop predictive models for cancer detection and prognosis.

Usage:

• Cancer diagnosis: Develop machine learning models to classify tumors as benign or malignant based on their characteristics, aiding in early detection and treatment planning.
• Feature importance analysis: Identify key attributes contributing to tumor malignancy and understand their biological significance.
• Clinical decision support: Assist healthcare professionals in interpreting biopsy results and making informed decisions about patient care.

Acknowledgements:

The Breast Cancer Wisconsin dataset is sourced from tissue samples collected for diagnostic purposes, with attributes derived from microscopic examination. The dataset is anonymized and made available for research purposes, contributing to advancements in cancer diagnosis and treatment.

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.

Breast Cancer Diagnosis

Study breast cancer diagnosis

By UCI [source]

About this dataset

This dataset contains data on breast cancer diagnosis, a devastating medical condition that affects thousands of people around the world each year. The data is comprised of patient ID, diagnosis (Malignant or Benign), and 30 computed features extracted from a digitized image of a fine needle aspirate (FNA) of a breast mass. Features include radius, texture, perimeter, area, smoothness, compactness concavity and concave points as well as symmetry and fractal dimension.

Created by renowned researchers in the fields of General Surgery and Computer Science at the University of Wisconsin-Madison led by Dr. William H Wolberg with contributions from Professor W Nick Street and Olvi L Mangasarian this dataset was used in some groundbreaking research to predict breast cancer prognosis using linear programming methods. More recently statistical methods such as support vector machines have been employed to classify tumour types from this dataset as well other tasks such as identify hidden patterns through pattern recognition techniques like Artificial Neural Networks (ANN).

It has also been used for studies exploring unsupervised classification tools like Ant Colony Optimization for discovering meaningful relationships among different variables which can help physicians better understand the progression of certain types of tumors over time. For example types cardinality analysis allowed researchers to determine tumor’s heterogeneity before deciding on appropriate treatments potentially leading to improved prognosis success rates overall. This Wisconsin Breast Cancer Diagnostic dataset provides an invaluable resource to scientists working on preventing or curing this dreaded disease - a goal we all eagerly hope to achieve someday soon!

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How to use the dataset

Research Ideas

• Developing a classifier that can accurately predict breast cancer diagnoses based on the provided features.
• Clustering patient data with similar diagnosis to discover trends or connections between certain symptoms and diagnoses.
• Optimizing feature selection algorithms to identify the most relevant predictors of breast cancer diagnosis from a set of given cell nuclei features

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: unformatted-data.csv

File: wpbc.data.csv | Column name | Description | |:--------------|:--------------------------------| | 119513 | ID number (Integer) | | N | Diagnosis (Binary) | | 31 | Radius (Real-valued) | | 18.02 | Texture (Real-valued) | | 27.6 | Perimeter (Real-valued) | | 117.5 | Area (Real-valued) | | 1013 | Smoothness (Real-valued) | | 0.09489 | Compactness (Real-valued) | | 0.1036 | Concavity (Real-valued) | | 0.1086 | Symmetry (Real-valued) | | 0.07055 | Fractal Dimension (Real-valued) | | 0.1865 | Mean Intensity (Real-valued) | | 0.06333 | Standard Error (Real-valued) | | 0.6249 | Worst Radius (Real-valued) | | 1.89 | Worst Texture (Real-valued) | | 3.972 | Worst Perimeter (Real-valued) | | 71.55 | Worst Area (Real-valued) | | 0.004433 | Worst Smoothness (Real-valued) | | 0.01421 | Worst Compactness (Real-valued) | | 0.03233 | Worst Concavity (Real-valued) |

File: breast-cancer-wisconsin.data.csv | Column name | Description | |:--------------|:--------------------------------------| | 119513 | ID number (Integer) | | 1000025 | ID number (Integer) | | 1.1 | Uniformity of Cell Size (Integer) | | 1.2 | Uniformity of Cell Shape (Integer) | | 1.3 | Single Epithelial Cell Size (Integer) | | 1.4 | Bland Chromatin (Integer) | | 1.5 | Normal Nucleoli (Integer) | | 2.1 | Mitoses (Integer) |

File: wdbc.data.csv | Column name | Description | |:--------------|:----------------------------------------| | 842302 | Patient ID number (Integer Type) | | M | Diagnosis (Binary Type) | | **...