Cancer is one of the biggest health challenges worldwide. As of 2021, around 15% of all deaths were cancer deaths, making it one of the most common causes of death globally.

Cancers are a group of diseases in which abnormal cells multiply rapidly and can grow into tumors. They can develop in different parts of the body and, in some cases, spread to other organs through the blood and lymph systems.

As the global population grows larger and older, the number of cancer cases has also increased. However, the age-standardized death rate from cancer has declined over time in many countries — due to improvements in diagnosis, research, medical advances, and public health efforts, as well as reductions in risk factors such as smoking and some cancer-causing pathogens.

On this page, we explore global data and research on different types of cancer. This can help us better understand the risk factors for cancer, how cancer risks vary across the lifespan, how they differ worldwide, and how they have changed over time.

The number of new cases, age-standardized rates and average age at diagnosis of cancers 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). Cancer incidence rates are age-standardized using the direct method and the final 2011 Canadian postcensal population structure. Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

To investigate the global incidence of prostate cancer with special attention to the changing age structures. Data regarding the cancer incidence and population statistics were retrieved from the International Agency for Research on Cancer in World Health Organization. Eight developing and developed jurisdictions in Asia and the Western countries were selected for global comparison. Time series were constructed based on the cancer incidence rates from 1988 to 2007. The incidence rate of the population aged ≥ 65 was adjusted by the increasing proportion of elderly population, and was defined as the “aging-adjusted incidence rate”. Cancer incidence and population were then projected to 2030. The aging-adjusted incidence rates of prostate cancer in Asia (Hong Kong, Japan and China) and the developing Western countries (Costa Rica and Croatia) had increased progressively with time. In the developed Western countries (the United States, the United Kingdom and Sweden), we observed initial increases in the aging-adjusted incidence rates of prostate cancer, which then gradually plateaued and even decreased with time. Projections showed that the aging-adjusted incidence rates of prostate cancer in Asia and the developing Western countries were expected to increase in much larger extents than the developed Western countries.

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The "Cancer and Deaths Dataset: 1990~2019 Globally" is a comprehensive dataset containing information on cancer incidence and mortality rates across the world from 1990 to 2019.

The dataset is an excellent resource for researchers, healthcare professionals, and policymakers who are interested in understanding the global burden of cancer and its impact on populations.

[ Total 9 file , 160 columns, All the countries, 30Years data ]

>In 2017, 9.6 million people are estimated to have died from the various forms of cancer. Every sixth death in the world is due to cancer, making it the second leading cause of death – second only to cardiovascular diseases.1

Progress against many other causes of deaths and demographic drivers of increasing population size, life expectancy and — particularly in higher-income countries — aging populations mean that the total number of cancer deaths continues to increase. This is a very personal topic to many: nearly everyone knows or has lost someone dear to them from this collection of diseases.

## Data vastness of this dataset: 01. annual-number-of-deaths-by-cause data. 02. total-cancer-deaths-by-type data. 03. cancer-death-rates-by-age data. 04. share-of-population-with-cancer-types data. 05. share-of-population-with-cancer data. 06. number-of-people-with-cancer-by-age data. 07. share-of-population-with-cancer-by-age data. 08. disease-burden-rates-by-cancer-types data. 09. cancer-deaths-rate-and-age-standardized-rate-index data.

In 2010, cancer deaths accounted for more than 15% of all deaths worldwide, and this fraction is estimated to rise in the coming years. Increased cancer mortality has been observed in immigrant populations, but a comprehensive analysis by country of birth has not been conducted. We followed all individuals living in Sweden between 1961 and 2009 (7,109,327 men and 6,958,714 women), and calculated crude cancer mortality rates and age-standardized rates (ASRs) using the world population for standardization. We observed a downward trend in all-site ASRs over the past two decades in men regardless of country of birth but no such trend was found in women. All-site cancer mortality increased with decreasing levels of education regardless of sex and country of birth (p for trend

Abstract

HANCOCK is a comprehensive, monocentric dataset of 763 head and neck cancer patients, including diverse data modalities. It contains histopathology imaging (whole-slide images of H&E-stained primary tumors and tissue microarrays with immunohistochemical staining) alongside structured clinical data (demographics, tumor pathology characteristics, laboratory blood measurements) and textual data (de-identified surgery reports and medical histories). All patients were treated curatively, and data span diagnoses from 2005–2019. This multimodal collection enables research into integrative analyses – for example, combining histologic features with clinical parameters for outcome prediction. Early analyses have demonstrated that fusing these modalities improves prognostic modeling compared to single-source data, and that leveraging histology with foundation models can enhance endpoint prediction​. HANCOCK aims to facilitate precision oncology studies by providing a large public resource for developing and benchmarking multimodal machine learning methods in head and neck cancer.

Introduction

Head and neck cancer (HNC) is a prevalent malignancy with poor outcomes – it is the 7th most common cancer globally and carries a 5-year survival of only ~25–60% despite modern treatments​. Improving patient prognosis may require personalized, multimodal therapy decisions, using information from pathology, clinical, and other data sources​. However, progress in multimodal prediction has been limited by the lack of large public datasets that integrate these diverse data types​. To our knowledge, existing HNC datasets are either small or incomplete; for example, a radiomics study included 288 oropharyngeal cases​, and a proteomics-focused set with imaging had only 122 cases​. The Cancer Genome Atlas (TCGA) provides multi-omics for >500 HNC cases, but lacks crucial data like pathology reports, blood tests, or comprehensive imaging for each patient​. These limitations hinder robust multimodal research​.

HANCOCK was created to address this gap​. It aggregates 763 patients’ data from a single academic center, capturing a real-world, uniformly treated cohort. The dataset uniquely combines whole slide histopathology images, tissue microarray images, detailed clinical parameters, pathology reports, and lab values in one resource​​. By curating and harmonizing these modalities, HANCOCK enables researchers to explore complex data interdependencies and develop multimodal predictive models. The patient population reflects typical HNC demographics – 80% male, median age 61, with 72% being former or current smokers​ – aligning with expected epidemiology​ and supporting generalizability. In summary, HANCOCK is an unprecedented multimodal HNC dataset that can fuel research in machine learning, prognostic biomarker discovery, and integrative oncology, ultimately advancing personalized head and neck cancer care.

Methods

The following sections describe how the HANCOCK data were collected, processed, and prepared for public sharing.

Subject Inclusion and Exclusion Criteria

Patients included in HANCOCK were those diagnosed with head and neck cancer between 2005 and 2019 at University Hospital Erlangen (Germany) who underwent a curative-intent initial treatment (surgery and/or definitive therapy)​. This encompasses cancers of the oral cavity, oropharynx, hypopharynx, and larynx​. Patients treated palliatively or with recurrent/metastatic disease at presentation were excluded to focus on first-course, curative treatments. The cohort consists of 763 patients (approximately 80% male, 20% female) with a median age of 61 years​. Notably, ~72% have a history of tobacco use​, which is consistent with real-world HNC risk factors. The distribution of tumor subsites and stages reflects typical HNC presentation, and thus the dataset is broadly representative of the general HNC patient population​. Being a single-center dataset, there is limited geographic diversity; however, the homogeneous data acquisition and treatment context reduce variability in data quality. No significant selection biases were introduced aside from the exclusion of non-curative cases – all major HNC subsite cases over the inclusion period were captured, providing a comprehensive real-world sample. Ethical approval was obtained for this retrospective data collection and sharing (Ethics Committee vote #23-22-Br), and all data were fully de-identified prior to release.

Data Acquisition

Histopathology: Tissue specimens from the primary tumors (and involved lymph nodes, if present) were obtained from the pathology archives. All samples were formalin-fixed and paraffin-embedded (FFPE) and stained with hematoxylin and eosin (H&E) following routine protocols​. Digital whole-slide imaging was performed on these histology slides. A total of 709 H&E slides of primary tumor tissue (701 patients had one slide, 8 patients had two slides) were scanned at high resolution using a 3DHISTECH P1000 scanner at an effective 82.44× magnification (0.1213 µm/pixel). Additionally, 396 H&E slides of lymph node metastases were scanned, using two systems: an Aperio Leica GT450 at 40× (0.2634 µm/pixel) and the 3DHISTECH P1000 at ~51× (0.1945 µm/pixel). (Multiple scanners were utilized over the course of the project; all resulting images were cross-verified for quality.) The digital whole slide images (WSIs) are provided in the pyramidal Aperio SVS format, a TIFF-based format compatible with standard viewers.

In addition to full slides, tissue microarrays (TMAs) were constructed from each patient’s tumor block to sample important regions. For each case, two cylindrical core biopsies (diameter 1.5 mm) were taken – one from the tumor center and one from the invasive tumor front. These cores were assembled into TMA blocks and stained on separate slides with a panel of eight stains: H&E plus immunohistochemical (IHC) markers targeting various immune cells and tumor biomarkers. The IHC markers include CD3, CD8, CD56, CD68, CD163, PD-L1, and MHC-1, which label T cells (CD3, CD8), natural killer cells (CD56), monocytes/macrophages (CD68, CD163), and a tumor immune checkpoint ligand (PD-L1), as well as MHC class I expression. Each core appears on up to 8 stained TMA slides (one per stain), yielding up to 16 TMA images per patient (two cores × eight stains). In the dataset, TMA images are provided for both the tumor-center and tumor-front cores; these too are digitized high-resolution images (consistent microscope settings, ~40×). The combination of WSIs and TMAs yields a rich imaging dataset: 701 patients have at least one primary tumor WSI (62 patients lack WSIs due to unavailable tissue), and all patients have TMA core images unless the tumor block was exhausted. This imaging data offers both broad tissue context from WSIs and targeted cellular detail from TMAs. Manual tumor region annotations are also included for the primary tumor WSIs (see Data Analysis below).

Clinical and Pathology Data: A wide array of non-imaging data was extracted from hospital information systems and pathology reports for each patient. Key demographic variables (age, sex, etc.) and tumor pathology details were collected, including primary tumor site, histologic subtype, grade, TNM stage, resection margin status, depth of invasion, perineural and lymphovascular invasion, and nodal metastasis status. These pathology parameters were recorded in a structured format for each case​​. Standard clinical coding systems were used where applicable: e.g., diagnoses are coded with ICD-10 codes and procedures with OPS codes (the German procedure classification system)​. The dataset includes these codes for each patient’s conditions and treatments. Comprehensive laboratory blood test results at diagnosis or pre-treatment were also compiled, covering complete blood counts, coagulation measures, electrolytes, kidney function, C-reactive protein, and other relevant analytes. Reference ranges for each lab parameter are provided alongside the values to indicate whether a result was normal or abnormal. Most patients have a full panel of these lab results, though some values are missing if a test was not clinically indicated; the dataset notes availability per patient. All structured data have been cleaned and validated – for example, harmonizing category values and checking consistency (e.g. TNM stages align with recorded tumor sites).

Textual Data (Surgical Reports and Histories): Unstructured clinical text was also included to add rich context on treatment details. Surgery reports (operative notes) from the primary tumor resection and associated medical history summaries were retrieved from the hospital’s electronic records. For each patient, the operative report from their first definitive surgery and the corresponding

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.

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.

Background: This study aimed to evaluate the updated disease burden, risk factors, and temporal trends of liver cancer based on age, sex, and country. Methods: We estimated the incidence of liver cancer and its attribution to hepatitis B virus (HBV) and hepatitis C virus (HCV) in 2018 based on the Global Cancer Observatory and World Health Organization (WHO) Cancer Causes database. We extracted the prevalence of risk factors from the WHO Global Health Observatory to examine the associations by weighted linear regression. The trend analysis used data from the Cancer Incidence in Five Continents and the WHO mortality database from 48 countries. Temporal patterns of incidence and mortality were calculated using average annual percent change (AAPC) by joinpoint regression analysis. Results: The global incidence of liver cancer was (age-standardized rate [ASR]) 9.3 per 100,000 population in 2018, and there was an evident disparity in the incidence related to HBV (ASR 0.2–41.2) and HCV (ASR 0.4–43.5). A higher HCV/HBV-related incidence ratio was associated with a higher level of alcohol consumption (β 0.49), overweight (β 0.51), obesity (β 0.64), elevated cholesterol (β 0.70), gross domestic product (β 0.20), and Human Development Index (HDI; β 0.45). An increasing trend in incidence was identified in many countries, especially for male individuals, population aged ≥50 years, and countries with a higher HCV/HBV-related liver cancer incidence ratio. Countries with the most drastic increase in male incidence were reported in India (AAPC 7.70), Ireland (AAPC 5.60), Sweden (AAPC 5.72), the UK (AAPC 5.59), and Norway (AAPC 4.87). Conclusion: We observed an overall increasing trend of liver cancer, especially among male subjects, older individuals, and countries with a higher prevalence of HCV-related liver cancer. More efforts are needed in enhancing lifestyle modifications and accessibility of antiviral treatment for these populations. Future studies should investigate the reasons behind these epidemiological changes.

This dataset contains Cancer Incidence data for Lung 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 segmented by sex (Both Sexes, Male, and Female) and 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.

This database encompasses several files related to cancer data. The first file is an Excel spreadsheet, containing information on newly diagnosed cancer cases from 2014 to 2017. It provides demographic details and specific characteristics of 482,229 cancer patients. We categorized this data according to the International Agency for Research on Cancer (IARC) reporting rules, and cancers with greater incidence rates were identified. To create a geodatabase, individual data was integrated at the county level and combined with population data. Files 2 and 3 contain gender-specific spatial data for the top cancer types and non-melanoma skin cancer. Each file includes county identifications, the number of cancer cases for each cancer type per year, and gender-specific population information. Lastly, there is a user's guide file to help navigate through the data files.

High-risk human papillomavirus (hrHPV) infection is established as the major cause of invasive cervical cancer (ICC). However, whether hrHPV status in the tumor is associated with subsequent prognosis of ICC is controversial. We aim to evaluate the association between tumor hrHPV status and ICC prognosis using national registers and comprehensive human papillomavirus (HPV) genotyping.

In this nationwide population-based cohort study, we identified all ICC diagnosed in Sweden during the years 2002–2011 (4,254 confirmed cases), requested all archival formalin-fixed paraffin-embedded blocks, and performed HPV genotyping. Twenty out of 25 pathology biobanks agreed to the study, yielding a total of 2,845 confirmed cases with valid HPV results. Cases were prospectively followed up from date of cancer diagnosis to 31 December 2015, migration from Sweden, or death, whichever occurred first. The main exposure was tumor hrHPV status classified as hrHPV-positive and hrHPV-negative. The primary outcome was all-cause mortality by 31 December 2015. Five-year relative survival ratios (RSRs) were calculated, and excess hazard ratios (EHRs) with 95% confidence intervals (CIs) were estimated using Poisson regression, adjusting for education, time since cancer diagnosis, and clinical factors including age at cancer diagnosis and International Federation of Gynecology and Obstetrics (FIGO) stage.

Of the 2,845 included cases, hrHPV was detected in 2,293 (80.6%), and we observed 1,131 (39.8%) deaths during an average of 6.2 years follow-up. The majority of ICC cases were diagnosed at age 30–59 years (57.5%) and classified as stage IB (40.7%). hrHPV positivity was significantly associated with screen-detected tumors, young age, high education level, and early stage at diagnosis (p < 0.001). The 5-year RSR compared to the general female population was 0.74 (95% CI 0.72–0.76) for hrHPV-positive cases and 0.54 (95% CI 0.50–0.59) for hrHPV-negative cases, yielding a crude EHR of 0.45 (95% CI 0.38–0.52) and an adjusted EHR of 0.61 (95% CI 0.52–0.71). Risk of all-cause mortality as measured by EHR was consistently and statistically significantly lower for cases with hrHPV-positive tumors for each age group above 29 years and each FIGO stage above IA. The difference in prognosis by hrHPV status was highly robust, regardless of the clinical, histological, and educational characteristics of the cases. The main limitation was that, except for education, we were not able to adjust for lifestyle factors or other unmeasured confounders.

In conclusion, women with hrHPV-positive cervical tumors had a substantially better prognosis than women with hrHPV-negative tumors. hrHPV appears to be a biomarker for better prognosis in cervical cancer independent of age, FIGO stage, and histological type, extending information from already established prognostic factors. The underlying biological mechanisms relating lack of detectable tumor hrHPV to considerably worse prognosis are not known and should be further investigated.

Purpose:

To compile a comprehensive survival and HPV genotyping data and provide a large-scale population-based evaluation of the association between tumor high risk HPV status and prognosis of invasive cervical cancer.

This is an aggregated dataset (popmort_agg_2000_2015.dta) including the average survival rates of the Swedish female population, by age, for years 2000-2015. The dataset is generated based on the age-, gender- and calender year- specific survival rates of the Swedish population during the same calendar period.

The dataset included 4 variables: • Sex: Gender (all female): 2=female. • _age: Age (in years) • _year: Calendar year • Prob: Survival probability in corresponding age and calendar year

BackgroundClinical and sociodemographic characteristics of differentiated thyroid cancer (DTC) patients with synchronous bone metastasis (SBM) remain unclear. This real-world study aimed to elucidate the incidence and prognosis of DTC patients with SBM using population-based data.MethodsData of patients with newly diagnosed DTC from 2010 to 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariable logistic regression analysis was utilized to identify predictors of developing SBM in patients with DTC and was further evaluated by receiver operator characteristics (ROC) analysis. Multivariable Cox regression was applied to identify prognostic factors associated with overall survival (OS) and cancer-specific survival (CSS).ResultsA total of 67,176 patients with DTC were screened from the database, with 0.36% (244/67,176) developed SBM. The age-adjusted incidence of SBM in patients with DTC was relatively stable during the study period with an average annual percentage change (AAPC) of 2.52. Multivariable logistic regression analysis recognized seven factors (older age, male gender, black race, other races, follicular histology, the American Joint Committee on Cancer (AJCC) T2, T3, T4 staging, and N1 staging) as predictors of developing SBM among the entire cohort, with the value of area under the curve (AUC) of 0.931 (95% CI: 0.915–0.947). The median survival time of DTC patients with SBM was 22 months (interquartile range, 7–47 months). The multivariable Cox regression analysis indicated multiple metastatic sites, surgical procedures, and chemotherapy as predictors for the survival of patients.ConclusionsPredictors and prognostic factors of SBM in patients with DTC were identified in this study. Patients with risk factors should be given more attention in clinical practice.

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.

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

ObjectiveTo elucidate the historical trends, underlying causes and future projections of esophageal cancer incidence in Hong Kong.MethodsUtilizing the Age-Period-Cohort (APC) model, we analyzed data from the Hong Kong Cancer Registry (1992–2021) and United Nations World Population Prospects 2022 Revision. Age-standardized incidence rates were computed, and APC models evaluated age, period, and cohort effects. Bayesian APC modeling, coupled with decomposition analysis, projected future trends and identified factors influencing incidence.ResultsBetween 1992 and 2021, both crude and age-standardized incidence rates of esophageal cancer witnessed significant declines. Net drifts exhibited pronounced downward trends for both sexes, with local drift diminishing across all age groups. Period and cohort rate ratios displayed a consistent monotonic decline for both sexes. Projections indicate a continued decline in esophageal cancer incidence. Population decomposition analysis revealed that epidemiological changes offset the increase in esophageal cancer cases due to population growth and aging.ConclusionThe declining trend of esophageal cancer in Hong Kong is influenced by a combination of age, period, and cohort. Sustaining and enhancing these positive trends requires continuous efforts in public health interventions.

Latin Americans are highly heterogeneous regarding the type of Native American ancestry. Consideration of specific associations with common diseases may lead to substantial advances in unraveling of disease etiology and disease prevention. Here we investigate possible associations between the type of Native American ancestry and leading causes of death. After an aggregate-data study based on genome-wide genotype data from 1805 admixed Chileans and 639,789 deaths, we validate an identified association with gallbladder cancer relying on individual data from 64 gallbladder cancer patients, with and without a family history, and 170 healthy controls. Native American proportions were markedly underestimated when the two main types of Native American ancestry in Chile, originated from the Mapuche and Aymara indigenous peoples, were combined together. Consideration of the type of Native American ancestry was crucial to identify disease associations. Native American ancestry showed no association with gallbladder cancer mortality (P = 0.26). By contrast, each 1% increase in the Mapuche proportion represented a 3.7% increased mortality risk by gallbladder cancer (95%CI 3.1–4.3%, P = 6×10−27). Individual-data results and extensive sensitivity analyses confirmed the association between Mapuche ancestry and gallbladder cancer. Increasing Mapuche proportions were also associated with an increased mortality due to asthma and, interestingly, with a decreased mortality by diabetes. The mortality due to skin, bladder, larynx, bronchus and lung cancers increased with increasing Aymara proportions. Described methods should be considered in future studies on human population genetics and human health. Complementary individual-based studies are needed to apportion the genetic and non-genetic components of associations identified relying on aggregate-data.

Death rate of a population adjusted to a standard age distribution. As most causes of death vary significantly with people's age and sex, the use of standardised death rates improves comparability over time and between countries, as they aim at measuring death rates independently of different age structures of populations. The standardised death rates used here are calculated on the basis of a standard European population (defined by the World Health Organization). Detailed data for 65 causes of death are available in the database (under the heading 'Data').

Lung Cancer Diagnostic Tests Market Outlook

The lung cancer diagnostic tests market size was valued at USD 2.5 billion in 2023 and is projected to reach USD 6.1 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 10.5% during the forecast period. This substantial growth can be attributed to the rising prevalence of lung cancer globally, advancements in diagnostic technologies, and increasing awareness regarding early detection and treatment of lung cancer. The growing aging population and the high incidence of smoking, which is a leading cause of lung cancer, further propel the demand for diagnostic tests.

The increasing prevalence of lung cancer is one of the primary drivers of market growth. Lung cancer remains the leading cause of cancer-related deaths worldwide, necessitating the development of more accurate and early diagnostic methods. With advancements in medical technology, such as molecular diagnostics and non-invasive imaging techniques, the accuracy and efficiency of lung cancer diagnosis have significantly improved. These innovations not only enhance the detection rate but also facilitate personalized treatment plans, thereby improving patient outcomes.

Furthermore, government initiatives and funding for cancer research play a crucial role in market expansion. Many countries are investing heavily in cancer research, leading to the development of new diagnostic tools and techniques. For instance, organizations such as the National Cancer Institute (NCI) in the United States provide substantial grants for lung cancer research, fostering innovations in diagnostics. In addition, public awareness campaigns and screening programs conducted by healthcare organizations and governments encourage early diagnosis, which is vital for successful treatment and survival rates.

The integration of artificial intelligence (AI) and machine learning in diagnostic tools is another significant factor contributing to market growth. AI algorithms can analyze medical images with high precision, aiding radiologists in identifying lung cancer at earlier stages. Moreover, AI-driven software can evaluate large datasets from genetic and molecular tests, providing insights into the most effective treatment options based on individual patient profiles. This technological advancement not only enhances the accuracy of diagnostics but also reduces the time required for analysis, thereby increasing the efficiency of healthcare services.

The EGFR Mutation Test is a pivotal advancement in the realm of lung cancer diagnostics, offering a more personalized approach to treatment. This test specifically identifies mutations in the Epidermal Growth Factor Receptor (EGFR) gene, which are often present in non-small cell lung cancer (NSCLC) patients. By detecting these mutations, healthcare providers can tailor therapies that target the specific genetic alterations, thereby improving treatment efficacy and patient outcomes. The growing adoption of EGFR Mutation Tests underscores the shift towards precision medicine, where treatments are increasingly customized based on individual genetic profiles. This approach not only enhances the effectiveness of therapies but also minimizes adverse effects, as treatments are more accurately aligned with the patient's unique genetic makeup.

Regionally, North America holds the largest share of the lung cancer diagnostic tests market, followed by Europe and Asia Pacific. The dominance of North America can be attributed to the presence of advanced healthcare infrastructure, high healthcare expenditure, and a robust research landscape. The Asia Pacific region, however, is expected to witness the highest growth rate during the forecast period, driven by increasing healthcare investments, growing awareness about lung cancer, and rising incidences of the disease in countries like China and India. The growing middle-class population and improving healthcare access in these countries further support market growth.

Test Type Analysis

The lung cancer diagnostic tests market is segmented by test type into imaging tests, sputum cytology, tissue biopsy, molecular tests, and others. Imaging tests are one of the most commonly used diagnostic methods for lung cancer detection. Techniques such as X-rays, CT scans, and PET scans provide detailed visuals of the lungs, helping in identifying abnormal growths or tumors. The non-invasive nature of these tests and their ability to provide quick results make them a preferred choice among healthcare