The United States Cancer Statistics (USCS) online databases in WONDER provide cancer incidence and mortality data for the United States for the years since 1999, by year, state and metropolitan areas (MSA), age group, race, ethnicity, sex, childhood cancer classifications and cancer site. Report case counts, deaths, crude and age-adjusted incidence and death rates, and 95% confidence intervals for rates. The USCS data are the official federal statistics on cancer incidence from registries having high-quality data and cancer mortality statistics for 50 states and the District of Columbia. USCS are produced by the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI), in collaboration with the North American Association of Central Cancer Registries (NAACCR). Mortality data are provided by the Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Vital Statistics System (NVSS).

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”.

Age-standardised rate of mortality from oral cancer (ICD-10 codes C00-C14) in persons of all ages and sexes per 100,000 population.RationaleOver the last decade in the UK (between 2003-2005 and 2012-2014), oral cancer mortality rates have increased by 20% for males and 19% for females1Five year survival rates are 56%. Most oral cancers are triggered by tobacco and alcohol, which together account for 75% of cases2. Cigarette smoking is associated with an increased risk of the more common forms of oral cancer. The risk among cigarette smokers is estimated to be 10 times that for non-smokers. More intense use of tobacco increases the risk, while ceasing to smoke for 10 years or more reduces it to almost the same as that of non-smokers3. Oral cancer mortality rates can be used in conjunction with registration data to inform service planning as well as comparing survival rates across areas of England to assess the impact of public health prevention policies such as smoking cessation.References:(1) Cancer Research Campaign. Cancer Statistics: Oral – UK. London: CRC, 2000.(2) Blot WJ, McLaughlin JK, Winn DM et al. Smoking and drinking in relation to oral and pharyngeal cancer. Cancer Res 1988; 48: 3282-7. (3) La Vecchia C, Tavani A, Franceschi S et al. Epidemiology and prevention of oral cancer. Oral Oncology 1997; 33: 302-12.Definition of numeratorAll cancer mortality for lip, oral cavity and pharynx (ICD-10 C00-C14) in the respective calendar years aggregated into quinary age bands (0-4, 5-9,…, 85-89, 90+). This does not include secondary cancers or recurrences. Data are reported according to the calendar year in which the cancer was diagnosed.Counts of deaths for years up to and including 2019 have been adjusted where needed to take account of the MUSE ICD-10 coding change introduced in 2020. Detailed guidance on the MUSE implementation is available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/articles/causeofdeathcodinginmortalitystatisticssoftwarechanges/january2020Counts of deaths for years up to and including 2013 have been double adjusted by applying comparability ratios from both the IRIS coding change and the MUSE coding change where needed to take account of both the MUSE ICD-10 coding change and the IRIS ICD-10 coding change introduced in 2014. The detailed guidance on the IRIS implementation is available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/impactoftheimplementationofirissoftwareforicd10causeofdeathcodingonmortalitystatisticsenglandandwales/2014-08-08Counts of deaths for years up to and including 2010 have been triple adjusted by applying comparability ratios from the 2011 coding change, the IRIS coding change and the MUSE coding change where needed to take account of the MUSE ICD-10 coding change, the IRIS ICD-10 coding change and the ICD-10 coding change introduced in 2011. The detailed guidance on the 2011 implementation is available at https://webarchive.nationalarchives.gov.uk/ukgwa/20160108084125/http://www.ons.gov.uk/ons/guide-method/classifications/international-standard-classifications/icd-10-for-mortality/comparability-ratios/index.htmlDefinition of denominatorPopulation-years (aggregated populations for the three years) for people of all ages, aggregated into quinary age bands (0-4, 5-9, …, 85-89, 90+)

SEER Limited-Use cancer incidence data with associated population data. Geographic areas available are county and SEER registry. The Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute collects and distributes high quality, comprehensive cancer data from a number of population-based cancer registries. Data include patient demographics, primary tumor site, morphology, stage at diagnosis, first course of treatment, and follow-up for vital status. The SEER Program is the only comprehensive source of population-based information in the United States that includes stage of cancer at the time of diagnosis and survival rates within each stage.

In the following maps, the U.S. states are divided into groups based on the rates at which people developed or died from cancer in 2013, the most recent year for which incidence data are available.

The rates are the numbers out of 100,000 people who developed or died from cancer each year.

Incidence Rates by State The number of people who get cancer is called cancer incidence. In the United States, the rate of getting cancer varies from state to state.

*Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

‡Rates are not shown if the state did not meet USCS publication criteria or if the state did not submit data to CDC.

†Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Death Rates by State Rates of dying from cancer also vary from state to state.

*Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

†Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Source: https://www.cdc.gov/cancer/dcpc/data/state.htm

Project Title: Cancer Data Analysis for Improved Healthcare

Description:

Our Cancer Data Analysis project is a comprehensive effort aimed at harnessing the power of data to advance our understanding of cancer, improve patient care, and contribute to ongoing research in oncology. This project brings together a multidisciplinary team of researchers, data scientists, and healthcare professionals committed to making a positive impact on the fight against cancer.

Project Objectives:

Data Collection: We have compiled a diverse and extensive dataset containing information on cancer incidence, patient demographics, treatment outcomes, genomic profiles, and more. This dataset represents a valuable resource for researchers and healthcare providers.

Insights and Trends: Through advanced data analysis techniques, we aim to uncover meaningful insights into cancer trends, including the prevalence of different cancer types, regional variations, and changes over time. These insights can inform healthcare policies and resource allocation.

Treatment Optimization: By analyzing treatment outcomes and patient responses to various therapies, we aim to identify patterns that can help tailor cancer treatment plans to individual patient needs, ultimately improving survival rates and quality of life.

Epidemiological Insights: We analyze epidemiological data to track the spread of cancer

Impact:

The Cancer Data Analysis project aspires to make a significant impact on cancer research, clinical practice, and public health initiatives. By providing valuable data and insights, we hope to contribute to:

Early cancer detection and diagnosis Improved treatment protocols Enhanced patient care and support Informed healthcare policy decisions Accelerated research breakthroughs

Collaboration:

We welcome collaboration with fellow researchers, healthcare professionals, and organizations committed to the fight against cancer. Together, we can leverage data-driven approaches to drive positive change in the field of oncology.

Join us in our mission to combat cancer through data-driven insights and innovative solutions. Together, we can make a difference in the lives of cancer patients and their families.

Users can access data about cancer statistics, specifically incidence and mortality worldwide for the 27 major types of cancer. Background Cancer Mondial is maintained by the Section of Cancer Information (CIN) of International Agency for Research on Cancer by the World Health Organization. Users can access CIN databases including GLOBOCAN, CI5(Cancer Incidence in Five Continents), WHO, ACCIS(Automated Childhood Cancer Information System), ECO (European Cancer Observatory), NORDCAN and Survcan. User functionality Users can access a variety of databases. CIN Databases: GLOBOCAN provides acces s to the most recent estimates (for 2008) of the incidence of 27 major cancers and mortality from 27 major cancers worldwide. CI5 (Cancer Incidence in Five Continents) provides access to detailed information on the incidence of cancer recorded by cancer registries (regional or national) worldwide. WHO presents long time series of selected cancer mortality recorded in selected countries of the world. Collaborative projects: ACCIS (Automated Childhood Cancer Information System) provides access to data on cancer incidence and survival of children collected by European cancer registries. ECO (European Cancer Observatory) provides access to the estimates (for 2008) of the incidence of, and mortality f rom 25 major cancers in the countries of the European Union (EU-27). NORDCAN presents up-to-date long time series of cancer incidence, mortality, prevalence and survival from 40 cancers recorded by the Nordic countries. SurvCan presents cancer survival data from cancer registries in low and middle income regions of the world. 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.

Background Disruption of the balance between apoptosis and proliferation is considered to be an important factor in the development and progression of tumours. In the present study we determined the in vivo cell kinetics along the spectrum of apparently normal epithelium, hyperplasia, preinvasive lesions and invasive carcinoma, in breast tissues affected by fibrocystic changes in which preinvasive and/or invasive lesions developed, as a model of breast carcinogenesis. Materials and methods A total of 32 areas of apparently normal epithelium and 135 ductal proliferative and neoplastic lesions were studied. More than one epithelial lesion per case were analyzed. The apoptotic index (AI) and the proliferative index (PI) were expressed as the percentage of TdT-mediated dUTP-nick end-labelling (TUNEL) and Ki-67-positive cells, respectively. The PI/AI (P/A index) was calculated for each case. Results The AIs and PIs were significantly higher in hyperplasia than in apparently normal epithelium (P = 0.04 and P = 0.0005, respectively), in atypical hyperplasia than in hyperplasia (P = 0.01 and P = 0.04, respectively) and in invasive carcinoma than in in situ carcinoma (P < 0.001 and P < 0.001, respectively). The two indices were similar in atypical hyperplasia and in in situ carcinoma. The P/A index increased significantly from normal epithelium to hyperplasia (P = 0.01) and from preinvasive lesions to invasive carcinoma (P = 0.04) whereas it was decreased (non-significantly) from hyperplasia to preinvasive lesions. A strong positive correlation between the AIs and the PIs was found (r = 0.83, P < 0.001). Conclusion These findings suggest accelerating cell turnover along the continuum of breast carcinogenesis. Atypical hyperplasias and in situ carcinomas might be kinetically similar lesions. In the transition from normal epithelium to hyperplasia and from preinvasive lesions to invasive carcinoma the net growth of epithelial cells results from a growth imbalance in favour of proliferation. In the transition from hyperplasia to preinvasive lesions there is an imbalance in favour of apoptosis.

Cloud based data science infrastructure that provides secure access to cancer research data from NCI programs and key external cancer programs. Serves as coordinated resource for public data sharing of NCI funded programs. Users can explore and use analytical and visualization tools for data analysis. Enables to search and aggregate data across repositories including Cancer Data Service, Clinical Trial Data Commons, Genomic Data Commons, Imaging Data Commons, Integrated Canine Data Commons, Proteomic Data Commons.

The Get Data Out programme from the National Disease Registration Service publishes detailed statistics about small groups of cancer patients in a way that ensures patient anonymity is maintained. The 19 cancer sites currently covered by Get Data Out are: ‘Bladder, urethra, renal pelvis and ureter’, ‘Bone’, ‘Brain’, ‘Eye’, ‘Blood cancer (haematological neoplasms)’, ‘Blood cancer (haematological neoplasm) transformations’, ‘Head and neck’, ‘Kaposi sarcoma’, ‘Kidney’, ‘Liver and biliary tract’, ‘Lung, mesothelioma, and other thoracic', Oesophagus and stomach’, ‘Ovary’, ‘Pancreas’, ‘Prostate’, ‘Sarcoma’, ‘Skin tumours’, ‘Soft tissue’, ‘Testes’. Anonymisation standards are designed into the data by aggregation at the outset. Patients diagnosed with a certain type of tumour are divided into many smaller groups, each of which contains approximately 100 patients with the same characteristics. These groups are aimed to be clinically meaningful and differ across cancer sites. For each group of patients, Get Data Out routinely publish statistics about incidence, routes to diagnosis, treatments and survival. This release covers the addition of the diagnosis year 2022 for treatment, plus a refresh of the 2013-2021 treatment data. This is also a first release of a new 'Visualisations' tab on our dashboard which will allow the user to explore the GDO data in graphical and tabular form. Users will now be able to select a single GDO group using drop down menus and display figures of incidence, demographic, treatment, routes to diagnosis, and survival statistics by diagnosis year. Finally, this is a small update to the 2013-2022 incidence data to include more age standardised rates (ASRs) for gender specific groups (genital skin groups for example which previously did not have an ASR published). All releases and documentation are available on the Get Data Out dashboard. Before using the data, we recommend that you read the 'Introduction', 'FAQs' and 'Known limitations' tabs. The data is available in an open format for anyone to access and use. We hope that by releasing anonymous detailed data like this we can help researchers, the public and patients themselves discover more about cancer. If you have feedback or any other queries about Get Data Out, please email us at NDRSenquires@nhs.net and mention 'Get Data Out' in your email.

Existing epidemiologic reports or studies of cancer statistics in Korea lack sufficient data on cancer severity distributions and observed survival rates. This study analyzed trends in major cancer statistics according to sex and severity levels in Korea from 2006 to 2013. We included eight cancers (hepatocellular carcinoma, and thyroid, colorectal, gastric, lung, prostate, breast, and cervical cancer), using Korea Central Cancer Registry data. Severity level was classified by Surveillance, Epidemiology, and End Results (SEER) stage as follows: localized, regional, distant, or unknown. Numbers of incident cancer cases from 2006 to 2013 were described by sex and SEER stage. We estimated up to 8-year observed survival rates of major cancers by sex and SEER stage, and provided prevalence rates by sex and SEER stage in 2011, 2012, and 2013. Although increases in new cancer cases are slowing and the total number of incident cancer cases in 2013 decreased for the first time since 2006, the number of prevalent cancer cases was 663,530 in 2013, an increase of 13.3% compared to 2011. Among the five cancers affecting both sexes, sex-related differences in 5-year observed survival rates for lung cancer were greatest in the localized stage (men, 31.9%; women, 48.1%), regional stage (men, 20.0%; women, 31.3%), and unknown stage (men, 24.3%; women, 37.5%). The sum of the proportions of localized and regional stages for thyroid and breast cancer was over 90% in 2013, while the sum of the proportions of localized and regional stages for lung cancer was only 56.7% in 2013. Differences in observed survival rates between men and women were prominent in lung cancer for all SEER stages. The reported epidemiologic data from this study can be used to obtain a more valid measure of cancer burden using a summary measure of population health.

The relative distribution and prevalence of cancer cases by type in the All of Us Research Program from self-reported survey data and electronic health record overall.

These cancer data were collected from Global Cancer Statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries and the World Cancer Research Fund, which contain gender and country-classified data on mortality and incidence.

Some data sheets with ASR - Age-standardized rate (ASR) *Age-standardized rate (ASR) is a summary of the cancer rate in a population, adjusted for age. It's used to compare cancer rates across populations with different age structures.

The Get Data Out programme from the National Disease Registration Service publishes detailed statistics about small groups of cancer patients in a way that ensures patient anonymity is maintained. The Get Data Out programme currently covers 15 cancer sites. This data release is a corrected re-release of detailed statistics for 2013-2019 treatment data. The correction means that surgery counts are no longer slightly underreported. There are some small changes in group sizes of usually no more than 2%, although this is larger for non-melanoma skin cancers. The 15 cancer sites now covered by Get Data Out are: ‘Bladder, Urethra, Renal Pelvis and Ureter’, ‘Bone cancer’, ‘Brain, meningeal and other primary CNS tumours’, ‘Eye cancer’, ‘Head and neck’, ‘Kaposi sarcoma’, ‘Kidney’, ‘Oesophageal and Stomach’, ‘Ovary, fallopian tube and primary peritoneal carcinomas’, ‘Pancreas’, ‘Prostate’, ‘Sarcoma’, ‘Skin tumours’, ‘Soft tissue and peripheral nerve cancer’, ‘Testicular tumours including post-pubertal teratomas’. Anonymisation standards are designed into the data by aggregation at the outset. Patients diagnosed with a certain type of tumour are divided into many smaller groups, each of which contains approximately 100 patients with the same characteristics. These groups are aimed to be clinically meaningful and differ across cancer sites. For each group of patients, Get Data Out routinely publish statistics about incidence, routes to diagnosis, treatments and survival. All releases and documentation are available on the Get Data Out main technical page. Before using the data, we recommend that you read the guide for first time users. The data is available in an open format for anyone to access and use. We hope that by releasing anonymous detailed data like this we can help researchers, the public and patients themselves discover more about cancer. If you have feedback or any other queries about Get Data Out, please email us at NDRSenquires@nhs.net and mention 'Get Data Out' in your email.

This database of cancer-related citations for publications authored by CDC’s Division of Cancer Prevention and Control (DCPC) staff, fosters collaboration among scientists throughout the world. Allows for searching for links to scientific articles authored or co-authored by researchers from DCPC since 2000.

Background The contribution of BRCA1 and BRCA2 to the incidence of male breast cancer (MBC) in the United Kingdom is not known, and the importance of these genes in the increased risk of female breast cancer associated with a family history of breast cancer in a male first-degree relative is unclear. Methods We have carried out a population-based study of 94 MBC cases collected in the UK. We screened genomic DNA for mutations in BRCA1 and BRCA2 and used family history data from these cases to calculate the risk of breast cancer to female relatives of MBC cases. We also estimated the contribution of BRCA1 and BRCA2 to this risk. Results Nineteen cases (20%) reported a first-degree relative with breast cancer, of whom seven also had an affected second-degree relative. The breast cancer risk in female first-degree relatives was 2.4 times (95% confidence interval [CI] = 1.4–4.0) the risk in the general population. No BRCA1 mutation carriers were identified and five cases were found to carry a mutation in BRCA2. Allowing for a mutation detection sensitivity frequency of 70%, the carrier frequency for BRCA2 mutations was 8% (95% CI = 3–19). All the mutation carriers had a family history of breast, ovarian, prostate or pancreatic cancer. However, BRCA2 accounted for only 15% of the excess familial risk of breast cancer in female first-degree relatives. Conclusion These data suggest that other genes that confer an increased risk for both female and male breast cancer have yet to be found.

Explore the booming Cancer Registry Data Management Software market, projected to exceed USD 5,800 million by 2033. Discover key drivers like rising cancer incidence, cloud adoption, and essential applications for hospitals and government organizations.

List of Top Disciplines of Cancer Research Statistics and Treatment sorted by citations.

Abstract Objective: To identify the socioepidemiologic and histopathologic patterns of lung cancer patients in the Middle Euphrates region. Patients and Methods: This study analyzed medical information from lung cancer patients at the Middle Euphrates Cancer Center in Iraq from January 2018 to December 2023. Demographic information (age, gender, residency, and education level) as well as clinical details (histopathological categorization) were obtained. The inclusion criteria included all confirmed lung cancer cases, while cases with inadequate data or non-lung cancer diagnosis were omitted. The data were analyzed using IBM SPSS Statistics (version 26). The data summarized using descriptive statistics, and chi-square tests used to identify correlations between categorical variables at a significance level of p < 0.05. Ethical approval was obtained from the relevant institutional review board. Results: A total of 1162 patients were included with mean age at diagnosis(64.47±11.45) years. Majority of patients are over 60 years (64.4%), followed by (40–60 years), 34%, and the least affected group is under 40 years (1.6%). Males account for the majority of cases (68%), while females about 32%, with male:female ratio that fluctuate around 2:1. Illiterate patients and those with low education levels represent the largest proportion accounting for about 87.9% of the study population. Squamous Cell Carcinoma (SCC) is the most frequent subtype (41.7%), followed closely by Adenocarcinoma (AC) at 37%, and Small Cell Lung Cancer (SCLC), 10.5%. Although SCC is the predominant subtype overall, AC incidence is increasing overtime (from 31.7% in 2018 to 41.4% in 2023) with predominance in females, younger and higher educated groups. While the percentage of SCLC and other less common subgroups remained relatively stable over time, there is a significant reduction in NSCLC-NOS diagnoses (from 11.1% in 2018 to 3.2% in 2023). Conclusions: In Iraq, specifically in the Middle Euphrates region, lung cancer is a major public health issue in the elder age groups. The two main subtypes, SCC and AC, are the main contributors, with obvious increment in AC cases in the recent years. The shifting trends indicate the urgent need for improved screening strategies, focused preventative initiatives, and customized treatment plans in view of changing risk profiles.

Discover the booming market for Cancer Registry Data Management Software. This in-depth analysis reveals key trends, growth drivers, and leading companies shaping this $2.5B+ market, projected for significant expansion through 2033. Learn about market segmentation, regional insights, and competitive analysis.