In 2025, it was estimated that there would be over 972 thousand new cancer cases among women in the United States. This statistic illustrates the estimated number of new cancer cases and deaths in the United States for 2025, by gender.

Cancer was responsible for around *** deaths per 100,000 population in the United States in 2023. The death rate for cancer has steadily decreased since the 1990’s, but cancer still remains the second leading cause of death in the United States. The deadliest type of cancer for both men and women is cancer of the lung and bronchus which will account for an estimated ****** deaths among men alone in 2025. Probability of surviving Survival rates for cancer vary significantly depending on the type of cancer. The cancers with the highest rates of survival include cancers of the thyroid, prostate, and testis, with five-year survival rates as high as ** percent for thyroid cancer. The cancers with the lowest five-year survival rates include cancers of the pancreas, liver, and esophagus. Risk factors It is difficult to determine why one person develops cancer while another does not, but certain risk factors have been shown to increase a person’s chance of developing cancer. For example, cigarette smoking has been proven to increase the risk of developing various cancers. In fact, around ** percent of cancers of the lung, bronchus and trachea among adults aged 30 years and older can be attributed to cigarette smoking. Other modifiable risk factors for cancer include being obese, drinking alcohol, and sun exposure.

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.

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)

In 2022, 83.2 males and 69.3 females per 100,000 population in England were registered as newly diagnosed with malignant neoplasm of bronchus and lung. Over the analyzed years, the rate of newly diagnosed cases for male individuals has seen a decrease trend. Conversely, the rate of newly diagnosed cases for females has seen a steady increase over the years. This statistic shows the rate of newly diagnosed cases of lung cancer per 100,000 population in England from 1995 to 2022, by gender.

The graph presents prostate cancer relative survival rates in the U.S. from 2001 to 2016, showing 1-year, 5-year, and 10-year relative survival percentages based on age groups. The x-axis represents age groups, while the y-axis indicates survival rates at different time intervals. Survival rates remain high across all age groups, with patients aged 65–69 having the highest 10-year survival rate of 99.5%. In contrast, men aged 80 and older have the lowest survival rates, with 92.1% at 1 year and 82.7% at 10 years. The data highlights that younger patients generally experience better long-term survival outcomes.

The estimated number of new cancer cases in Mexico is forecast to steadily increase in the coming years. The incidence rate of cancer in the country is expected to reach nearly ******* new cases in 2050. This figure amounted to ******* cases in 2022. In that year, breast cancer was the most common type of cancer among newly diagnosed patients in the country.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024 Cancer Mortality Rate - This indicator shows the age-adjusted mortality rate from cancer (per 100,000 population). Maryland’s age adjusted cancer mortality rate is higher than the US cancer mortality rate. Cancer impacts people across all population groups, however wide racial disparities exist. Link to Data Details

Users can access data about cancer statistics in the United States including but not limited to searches by type of cancer and race, sex, ethnicity, age at diagnosis, and age at death. Background Surveillance Epidemiology and End Results (SEER) database’s mission is to provide information on cancer statistics to help reduce the burden of disease in the U.S. population. The SEER database is a project to the National Cancer Institute. The SEER database collects information on incidence, prevalence, and survival from specific geographic areas representing 28 percent of the United States population. User functionality Users can access a variety of reso urces. Cancer Stat Fact Sheets allow users to look at summaries of statistics by major cancer type. Cancer Statistic Reviews are available from 1975-2008 in table format. Users are also able to build their own tables and graphs using Fast Stats. The Cancer Query system provides more flexibility and a larger set of cancer statistics than F ast Stats but requires more input from the user. State Cancer Profiles include dynamic maps and graphs enabling the investigation of cancer trends at the county, state, and national levels. SEER research data files and SEER*Stat software are available to download through your Internet connection (SEER*Stat’s client-server mode) or via discs shipped directly to you. A signed data agreement form is required to access the SEER data Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available under “Data Documentation and Variable Recodes”.

In 2022, Australia had the fourth-highest total number of skin cancer cases worldwide and the highest age-standardized rate, with roughly 37 cases of skin cancer per 100,000 population. The graph illustrates the rate of skin cancer in the countries with the highest skin cancer rates worldwide in 2022.

📄 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.

Supporting figures and tables. Figure S1, Prevalence of smoking by age in 1950 birth cohort. Summary of shared input data (used by all 5 models) on smoking patterns for the US cohort born in 1950. Prevalence shown is estimated in the absence of lung cancer mortality. Version 1.0 of the Smoking History Generator (SHG) refers to published data through 2000 (Anderson, et al.), and version 1.5 supplies the 1950 birth cohort used for this analysis with data through 2009 and projections past 2009. Figure S2, Other-cause mortality, by smoking quintile, in 1950 birth cohort. These curves show the other-cause (non-lung cancer) mortality for never smokers and for current smokers by smoking quintile (Q, of cigarettes per day) for the male birth cohort of 1950, out to age 99. Former smokers are intermediate to current and never smokers. There is a similar plot for females. These were shared inputs used by all the models. Note that the rates of non-lung cancer mortality represent the US population, not trial (NLST or PLCO) participants. Figure S3, Prevalence of smoking by age in 1950 birth cohort. Output from one model showing smoking prevalence by age (calendar year), in a no screening scenario. Proportions of current/former/never smokers are in the presence of lung cancer mortality as well as all-cause mortality. Figure S4, Prevalence of smoking by age and pack-years in 1950 birth cohort. Output from one model showing smoking prevalence by category of pack-year and age. The proportion of the cohort by age that has accumulated the specified number of pack-years in the presence of lung cancer mortality and other-cause mortality. Figure S5, Incidence, no screening scenario, output from all models. For predictions past observed SEER data (over age 60) there are no observed data, but we used an age-period-cohort model to project past observed years (‘Projected’ red double line in plots below), which shows that the models are most divergent after age 85, when SEER data become most sparse. We cannot strictly compare incidence to that in prior birth cohorts since smoking patterns are dissimilar, and incidence varies by cohort. Figure S6, Mortality, no screening scenario, output from all models. The vertical line at age 90 indicates age at which all event counts (screens, deaths and deaths averted, and life years gained) were truncated for the analyses reported here. Although the models ranked programs similarly, there was variability in the total numbers of predicted lung cancer cases, deaths, and therefore lung cancer deaths prevented. The differences in rates in the no screening scenario in large part explains the predicted differences between models. The four models (E, F, S, and U) which use two-stage or multi-stage clonal expansion models have more similarly shaped curves than the fifth model (M), which does not use a clonal expansion component (see Table S1 in File S1). Figure S7, Results from all models analogous to Figure 1 in article. Figure S8, Results from all models analogous to Figure 2 in article. Figure S9, Secondary results with reduced operative candidacy with age. The dashed line denotes the efficiency frontier in the main analysis. Table S1, Additional Detail on Models. Table S2, Complete List of 120 Consensus Efficient Scenarios. Table S3, Comparison of Consensus Efficient Scenarios Identified Using Life-years Saved or Lung Cancer Deaths Avoided as Measure of Benefit. (DOCX)

In 2022, adults aged 65 to 74 years had the highest incidence of HPV-associated cancer in the United States, with a rate of around 40 per 100,000 people. This graph shows the rate of HPV-related cancers per 100,000 people in the United States in 2022, by age.

BackgroundCancer is one of the major causes of death and the projection of cancer incidences is essential for future healthcare resources planning. Joinpoint regression and average annual percentage change (AAPC) are common approaches for cancer projection, while time series models, traditional ways of trend analysis in statistics, were considered less popular. This study aims to compare these projection methods on seven types of cancers in 31 geographical jurisdictions.MethodsUsing data from 66 cancer registries in the World Health Organization, projection models by joinpoint regression, AAPC, and autoregressive integrated moving average with exogenous variables (ARIMAX) were constructed based on 20 years of cancer incidences. The rest of the data upon 20-years of record were used to validate the primary outcomes, namely, 3, 5, and 10-year projections. Weighted averages of mean-square-errors and of percentage errors on predictions were used to quantify the accuracy of the projection results.ResultsAmong 66 jurisdictions and seven selected cancers, ARIMAX gave the best 5 and 10-year projections for most of the scenarios. When the ten-year projection was concerned, ARIMAX resulted in a mean-square-error (or percentage error) of 2.7% (or 7.2%), compared with 3.3% (or 15.2%) by joinpoint regression and 7.8% (or 15.0%) by AAPC. All the three methods were unable to give reasonable projections for prostate cancer incidence in the US.ConclusionARIMAX outperformed the joinpoint regression and AAPC approaches by showing promising accuracy and robustness in projecting cancer incidence rates. In the future, developments in projection models and better applications could promise to improve our ability to understand the trend of disease development, design the intervention strategies, and build proactive public health system.

The State Cancer Profiles (SCP) web site provides statistics to help guide and prioritize cancer control activities at the state and local levels. SCP is a collaborative effort using local and national level cancer data from the Centers for Disease Control and Prevention's National Program of Cancer Registries (NPCR) and National Cancer Institute's Surveillance, Epidemiology and End Results Registries (SEER). SCP address select types of cancer and select behavioral risk factors for which there are evidence-based control interventions. The site provides incidence, mortality and prevalence comparison tables as well as interactive graphs and maps and support data. The graphs and maps provide visual support for deciding where to focus cancer control efforts.

The graph displays the death rates for U.S. adults aged from 45 to 64 years due to cancer and heart disease from 1999 to 2017. The highest rate recorded was in the year 1999 for both cancer and heart disease, whereas the lowest rates for cancer and heart disease were recorded in the years 2017 and 2011, respectively. Overall, rates of death for both causes have declined.

In 2022, adults aged 80 to 84 years had the highest incidence of alcohol-associated cancer in the United States, with a rate of around 577 per 100,000 people. This graph shows the rate of alcohol-related cancers per 100,000 people in the United States in 2022, by age.

In 2022, there were 18.7 deaths from prostate cancer per 100,000 men in the United States. This statistic shows the prostate cancer death rate in the United States from 1975 to 2022.

Our aim is to complement observer-dependent approaches of immune cell evaluation in microscopy images with reproducible measures for spatial composition of lymphocytic infiltrates. Analyzing such patterns of inflammation is becoming increasingly important for therapeutic decisions, for example in transplantation medicine or cancer immunology. We developed a graph-based assessment of lymphocyte clustering in full whole slide images. Based on cell coordinates detected in the full image, a Delaunay triangulation and distance criteria are used to build neighborhood graphs. The composition of nodes and edges are used for classification, e.g. using a support vector machine. We describe the variability of these infiltrates on CD3/CD20 duplex staining in renal biopsies of long-term functioning allografts, in breast cancer cases, and in lung tissue of cystic fibrosis patients. The assessment includes automated cell detection, identification of regions of interest, and classification of lymphocytic clusters according to their degree of organization. We propose a neighborhood feature which considers the occurrence of edges with a certain type in the graph to distinguish between phenotypically different immune infiltrates. Our work addresses a medical need and provides a scalable framework that can be easily adjusted to the requirements of different research questions.

Supporting information on methods used and results obtained, containing Tables S1 to S15 and Figures S1 to S7. Table S1, Distribution of stages at diagnosis of BC. Table S2, Relative risk of breast cancer based on age and breast density. Table S3, Prevalences of risk factors by age group for each category of breast density. Table S4, Characteristics of the 2,625 screening strategies analized. Table S5, The utilities for the general population and for women diagnosed with BC, either DCIS or invasive. Table S6, Model for false positives of non-invasive tests. Table S7, Model for false positives of invasive tests. Table S8, Distribution of stages at diagnosis of BC for screen-detected cases. Different overdiagnosis rates. Table S9, Linear regression model with dependent variable being the DCIS rate per women-year. Table S10, Cost-effectiveness and harm-benefit analysis. Lives extended. Table S11, Cost-effectiveness and harm-benefit analysis. Quality-adjusted life years (QALY). Table S12, Number of mammograms and detection rates for screen-detected and interval cases and program sensitivity by age groups. Invasive cancer (DCIS not included). Table S13, Distribution of stages at diagnosis of BC. Table S14, Sensitivity analysis. Changes in lives extended. Table S15, Sensitivity analysis. Changes in QALY. Figure S1, Incidence curves for twelve risk profiles grouped by risk level: (A) Low Risk, (B) Medium-Low Risk, (C) Medium-High Risk, and (D) High Risk. Graphic (E) shows the smoothed incidence rates for each risk group. Figure S2, Observed and smoothed DCIS rates over time in Catalonia (1983–2008). Figure S3, Index of mammography use (IMU) and smoothed DCIS rates over time in Catalonia (1983–2008). Figure S4, Cost-effectiveness and harm-benefit analyses for 2,625 early detection strategies, with uniform strategies marked. Effect measured in lives extended. Figure S5, Cost-effectiveness and harm-benefit analyses for 2,625 early detection strategies, with uniform strategies marked. Effect measured in QALY. Figure S6, Sensitivity analysis of a change in the risk groups distribution. Cost-effectiveness and harm-benefit analyses for 2,625 early detection strategies. Effect measured in lives extended. Figure S7, Sensitivity analysis of a change in the risk groups distribution. Cost-effectiveness and harm-benefit analyses for 2,625 early detection strategies. Effect measured in QALY. (PDF)