This summary brings together information on prostate cancer incidence, mortality and survival. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: Prostate cancer: the most common cancer in men in England

The data used in this example is sourced from a study conducted by Stamey et al. (1989). The study aimed to investigate the relationship between the level of prostate-specific antigen (PSA) and various clinical measures in a group of 97 men who were scheduled to undergo a radical prostatectomy. PSA is a protein that is produced by the prostate gland, and higher levels of PSA are often associated with a higher likelihood of having prostate cancer. The dataset provides valuable information for examining the correlation between PSA levels and other clinical factors in the context of prostate cancer.

source: https://web.stanford.edu/~hastie/ElemStatLearn/datasets/prostate.data

Deaths from prostate cancer - Directly age-Standardised Rates (DSR) per 100,000 population Source: Office for National Statistics (ONS) Publisher: Information Centre (IC) - Clinical and Health Outcomes Knowledge Base Geographies: Local Authority District (LAD), Government Office Region (GOR), National, Primary Care Trust (PCT), Strategic Health Authority (SHA) Geographic coverage: England Time coverage: 2005-07, 2007 Type of data: Administrative data

Descriptive statistics by prostate cancer aggressiveness and race.

Rate: Number of deaths due to prostate cancer per 100,000 male population.

Definition: Number of deaths per 100,000 males with malignant neoplasm (cancer) of the prostate as the underlying cause of death (ICD-10 code: C61).

Data Sources:

(1) Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File. CDC WONDER On-line Database accessed at http://wonder.cdc.gov/cmf-icd10.html

(2) Death Certificate Database, Office of Vital Statistics and Registry, New Jersey Department of Health

(3) Population Estimates, State Data Center, New Jersey Department of Labor and Workforce Development

BackgroundAmong prostate cancer (PC) patients, over 90% of distant metastases occur in the bone. PC treatments may be associated with side effects, including second primary malignancies (SPM). There is limited information on the incidence of SPM among men with bone metastatic PC (mPC) and among men with bone metastatic castration-resistant PC (mCRPC). We estimated overall survival and the incidence of SPM in men with mPC and mCRPC.MethodsIn the Prostate Cancer data Base Sweden, the National Prostate Cancer Register was linked to other national health care registers, 15,953 men with mPC in 1999–2011 were identified. Further, 693 men with mCRPC were identified. Outcomes were evaluated using stratified incidence rates, Kaplan-Meier estimators and Cox models.ResultsThe mean age among men with mPC was 73.9 years and in men with mCRPC 70.0 years. The median respective survivals were 1.5 (13,965 deaths) and 1.14 years (599 deaths), and average times since PC diagnosis 1.8 and 4.7 years. We observed 2,669 SPMs in men with mPC and 100 SPMs in men with mCRPC. The incidence rate of SPM per 1,000 person-years was 81.8 (78.8–85.0) for mPC and 115.6 (95.1–140.7) for mCRPC. High age, prior neoplasms, urinary tract infection, congestive heart failure, diabetes and renal disease were most strongly associated with increased mortality risk. Prior neoplasms and prior use of antineoplastic agents were most strongly associated with increased SPM risk. Several factors associated with increased mortality and SPM risks were more prevalent in the mCRPC cohort.ConclusionsOur results on mortality for men with mPC and mCRPC are in line with previous studies from the same time period. Investigation of factors associated with mortality and SPM in men with mPC and mCRPC can help to further understand these outcomes in the era prior to several new treatments have come available.

Cancer diagnoses and age-standardised incidence rates for all types of cancer by age and sex including breast, prostate, lung and colorectal cancer.

** Description**

This dataset contains data about lung cancer Mortality and is a comprehensive collection of patient information, specifically focused on individuals diagnosed with cancer. This dataset contains comprehensive information on 800,000 individuals related to lung cancer diagnosis, treatment, and outcomes. With 16 well-structured columns. This large-scale dataset is designed to aid researchers, data scientists, and healthcare professionals in studying patterns, building predictive models, and enhancing early detection and treatment strategies.

🌍 The Societal Impact of Lung Cancer

Lung cancer is not just a disease — it's a global crisis that steals time, health, and hope from millions of people every year. As the #1 cause of cancer deaths worldwide, it takes more lives annually than breast, colon, and prostate cancer combined.

But behind every statistic is a story:

A parent who never saw their child graduate.

A worker who had to leave their job too soon.

A community that lost a leader, a friend, a neighbor.

Why does this matter? Lung cancer often goes undetected until it's too late. It’s aggressive, silent, and devastating — especially in underserved areas where early detection is rare and treatment options are limited. It doesn’t just affect patients. It affects families, economies, and healthcare systems on a massive scale.

This dataset represents more than numbers. It represents 800,000 real-world stories — people who can help us unlock patterns, train models, and advance life-saving research.

By working with this data, you're not just analyzing a dataset — you're stepping into the fight against one of humanity’s deadliest diseases.

Let’s turn insight into impact. (😊The above descriptions is generated with the help of AI, Just wanted to share this dataset That all. Thank you)

Basic Metadata Note: condition new in 2017. *Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.

**Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native.

Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Code Source: ICD-9CM - AHRQ HCUP CCS v2015. ICD-10CM - AHRQ HCUP CCS v2018. ICD-10 Mortality - California Department of Public Health, Group Cause of Death Codes 2013; NHCS ICD-10 2e-v1 2017.

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

Demographic characteristics of New South Wales men diagnosed with prostate cancer in 1997 to 2007, comparing those who committed suicide with all men diagnosed with prostate cancer, number, percent, person years at risk and crude rate per 100,000 person years at risk.

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.

Cancer registrations for prostate cancer per 100,000 population. Directly standardised registration rate Source: Regional Cancer Registries, Office for National Statistics (ONS). Publisher: Information Centre (IC) - Clinical and Health Outcomes Knowledge Base Geographies: Local Authority District (LAD), Government Office Region (GOR), National, Strategic Health Authority (SHA) Geographic coverage: England Time coverage: 2004-2006 Type of data: Administrative data

This publication reports on newly diagnosed cancers registered in England during 2022. It includes this summary report showing key findings, spreadsheet tables with more detailed estimates, and a methodology document. Cancer registration estimates are provided for: • Incidence of cancer using groupings that incorporate both the location and type of cancer by combinations of gender, age, deprivation, and stage at diagnosis (where appropriate) for England, former Government office regions, Cancer alliances and Integrated care boards • Incidence and mortality (using ICD-10 3-digit codes) by gender and age group for England, former Government office regions, Cancer alliances and Integrated care boards This publication will report on 2022 cancer registrations only, trends will not be reported as the required re-stated populations for 2012 to 2020 are not expected to be published by the Office of National Statistics (ONS) until Winter 2024.

This dataset presents the footprint of cancer mortality data in Australia for all cancers combined, and six selected cancers (female breast cancer, colorectal cancer, cervical cancer, lung cancer, melanoma of the skin, and prostate cancer) with their respective ICD-10 codes. The data spans the years 2011 to 2015 and is aggregated to 2015 PHN boundaries based on the 2011 Australian Statistical Geography Standard (ASGS). The source of the mortality data is the Australia Cancer Database, the National Mortality Database and the National Death Index. Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by AIHW in the National Mortality Database. For more information, please visit the data source: AIHW - Cancer incidence and mortality in Australia by small geographic areas. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Colorectal deaths presented are underestimates. For further information on complexities in the measurement of bowel cancer in Australia, refer to the Australian Bureau of Statistics.

Modeling overdetection resulting from screening often uses the conventional competing risk model. This model assigns screen-detected cases dying from other causes as overdetection modeled by a one-jump process, which may not be true for the censored overdetected cases. To relax this restrictive assumption, accommodate a finite Markov process for overdetection, and dispense with long-term follow-up until death, we propose a generalized Coxian phase-type Markov process to distinguish the progressive latent multistate pathway from the nonprogressive (overdetected) latent multistate pathway. Various new likelihood functions were developed to estimate the transition parameters with the available data accrued at the time of diagnosis. The proportion of overdetected cancers by the cured model was further estimated by using parameters with and without distinguishing between the two latent pathways. While perturbation analyses were conducted by changing their parameters to assess their effects on overdetection, the results, including of asymptotic analyses, were very robust for an overdetection rate higher than 20% but not for low overdetection rates. These two scenarios were demonstrated by applying the Coxian phase-type model to prostate cancer and breast cancer screening, yielding a substantial proportion of overdetected prostate cancer (60%) attributed to the prostate specific antigen test and a small fraction of overdetected breast cancer (3%) detected by mammography. This kind of variation in overdetection elucidated by the Coxian phase-type Markov process provides new insights into the quantitative mechanisms producing overdetection, which is informative for evaluating the benefits and risks of various types of population-based cancer screening programs.

Describes the place of death from cancer in Scotland, by demographic characteristics including deprivation. Locations of death are home, hospice, NHS Acute hospital, other institution; covers the four major cancers of lung, breast, colorectal and prostate. Ten year trends are also presented. As from May 2010 these statistics can be designated as National Statistics products. Source agency: ISD Scotland (part of NHS National Services Scotland) Designation: National Statistics Language: English Alternative title: Place of Death from Cancer

BackgroundProstate cancer is currently the most frequently diagnosed malignancy in men and the second leading cause of cancer-related deaths in industrialized countries. Worldwide, an increase in prostate cancer incidence is expected due to an increased life-expectancy, aging of the population and improved diagnosis. Although the specific underlying mechanisms of prostate carcinogenesis remain unknown, prostate cancer is thought to result from a combination of genetic and environmental factors altering key cellular processes. To elucidate these complex interactions and to contribute to the understanding of prostate cancer progression and metastasis, analysis of large scale gene expression studies using bioinformatics approaches is used to decipher regulation of core processes. Methodology/Principal FindingsIn this study, a standardized quality control procedure and statistical analysis (http://www.arrayanalysis.org/) were applied to multiple prostate cancer datasets retrieved from the ArrayExpress data repository and pathway analysis using PathVisio (http://www.pathvisio.org/) was performed. The results led to the identification of three core biological processes that are strongly affected during prostate carcinogenesis: cholesterol biosynthesis, the process of epithelial-to-mesenchymal transition and an increased metabolic activity. ConclusionsThis study illustrates how a standardized bioinformatics evaluation of existing microarray data and subsequent pathway analysis can quickly and cost-effectively provide essential information about important molecular pathways and cellular processes involved in prostate cancer development and disease progression. The presented results may assist in biomarker profiling and the development of novel treatment approaches.

The cohort consists of 900 individuals with prostate cancer who came to an oncology center in southern Sweden for therapy. The collection of this study started in 2007 and is ongoing. A control group of 1,000 men recruited among accompanying spouses of female cancer patients also belong to this study. The participants have donated blood and answered questions relating to previous medications, height, weight, alcohol consumption, and smoking.

Purpose:

To study risk factors for prostate cancer

Background: Knowledge on missing data in a clinical cancer register is important to assess the validity of research results. For analysis of prostate cancer (Pca), risk category, a composite variable based on serum levels of prostate specific antigen (PSA), stage, and Gleason score, is crucial for treatment decisions and a strong determinant of outcome. The aim of this study was to assess the proportion and characteristics of men in the National Prostate Cancer Register (NPCR) of Sweden with unknown risk category. Material and methods: Men diagnosed with Pca between 1998 and 2012 registered in NPCR with known or unknown risk category were compared with respect to age, socioeconomic factors, comorbidity, cancer characteristics, cancer treatment, and mortality from Pca and other causes. Results: In total, 3315 of 129 391 (3%) men had unknown risk category. Compared to other men in NPCR, these men more often had a concomitant bladder cancer diagnosis, 19% versus 1%, diagnosis of benign prostatic hyperplasia 31% versus 5%, received unspecified Pca treatment 16% versus 3%, had higher comorbidity, Charlson Comorbidity Index 2 or higher, 34% versus 13%, and had lower Pca mortality 12% versus 30%, but similar mortality from other causes. Conclusion: Men with unknown risk category were rare in NPCR but distinctly different from other men in NPCR in many aspects including higher comorbidity and lower Pca mortality.

GWAS summary statistics for prostate cancer mortality