PcBaSe Sweden is a data base for clinical epidemiological prostate cancer research based on linkages between the National Prostate Cancer Register (NPCR) of Sweden, a nationwide population-based quality database and other nationwide registries. In the period 1996-2023, 246 500 cases have been registered in NPCR with detailed data on tumour characteristics and primary treatment available https://statistik.incanet.se/npcr/. In addition, there are five controls per case.

By use of the individually unique person identity number, the NPCR has been linked to the Swedish National Cancer Register, the Cause of Death Register, the Prescribed Drug Register, the National Patient Register, and the Acute Myocardial Infarction Register, the Register of the Total Population, the Longitudinal Integration database for health insurance and labour market studies (LISA), the Multi-Generation Register and several other population-based registers. Van Hemelrijck M, Garmo H, Wigertz A, Nilsson P, Stattin P. Cohort Profile Update: The National Prostate Cancer Register of Sweden and Prostate Cancer data Base-a refined prostate cancer trajectory, Int J Epidemiol, 2016 Feb;45(1):73-82.

Purpose:

To provide a platform for prostate cancer research. The data base allows for population-based observational studies with case-control, cohort, or longitudinal case only design that can be used for studies of pertinent issues of clinical importance.

Years of Life Lost (YLL) as a result of death 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

Links to code and bioRxiv pre-print:

1. Multi-lens Neural Machine (MLNM) Code

2. An AI-assisted Tool For Efficient Prostate Cancer Diagnosis (bioRxiv Pre-print)

Digitized hematoxylin and eosin (H&E)-stained whole-slide-images (WSIs) of 40 prostatectomy and 59 core needle biopsy specimens were collected from 99 prostate cancer patients at Tan Tock Seng Hospital, Singapore. There were 99 WSIs in total such that each specimen had one WSI. H&E-stained slides were scanned at 40× magnification (specimen-level pixel size 0·25μm × 0·25μm) using Aperio AT2 Slide Scanner (Leica Biosystems). Institutional board review from the hospital were obtained for this study, and all the data were de-identified.

Prostate glandular structures in core needle biopsy slides were manually annotated and classified using the ASAP annotation tool (ASAP). A senior pathologist reviewed 10% of the annotations in each slide, ensuring that some reference annotations were provided to the researcher at different regions of the core. It is to be noted that partial glands appearing at the edges of the biopsy cores were not annotated.

Patches of size 512 × 512 pixels were cropped from whole slide images at resolutions 5×, 10×, 20×, and 40× with an annotated gland centered at each patch. This dataset contains these cropped images.

This dataset is used to train two AI models for Gland Segmentation (99 patients) and Gland Classification (46 patients). Tables 1 and 2 illustrate both gland segmentation and gland classification datasets. We have put the two corresponding sub-datasets as two zip files as follows:

1. gland_segmentation_dataset.zip
2. gland_classification_dataset.zip

Table 1: The number of slides and patches in training, validation, and test sets for gland segmentation task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen.

Table 2: The number of slides and patches in training, validation, and test sets for gland classification task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen. The gland classification datasets are the subsets of the gland segmentation datasets. GS: Gleason Score. B: Benign. M: Malignant.

NB: Gland classification folder (gland_classification_dataset.zip) may contain extra patches, labels of which could not be identified from H&E slides. They were not used in the machine learning study.

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

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

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

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.

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.

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

BackgroundMany individuals undergoing cancer treatment experience substantial financial hardship, often referred to as financial toxicity (FT). Those undergoing prostate cancer treatment may experience FT and its impact can exacerbate disparate health outcomes. Localized prostate cancer treatment options include: radiation, surgery, and/or active surveillance. Quality of life tradeoffs and costs differ between treatment options. In this project, our aim was to quantify direct healthcare costs to support patients and clinicians as they discuss prostate cancer treatment options. We provide the transparent steps to estimate healthcare costs associated with treatment for localized prostate cancer among the privately insured population using a large claims dataset.MethodsTo quantify the costs associated with their prostate cancer treatment, we used data from the Truven Health Analytics MarketScan Commercial Claims and Encounters, including MarketScan Medicaid, and peer reviewed literature. Strategies to estimate costs included: (1) identifying the problem, (2) engaging a multidisciplinary team, (3) reviewing the literature and identifying the database, (4) identifying outcomes, (5) defining the cohort, and (6) designing the analytic plan. The costs consist of patient, clinician, and system/facility costs, at 1-year, 3-years, and 5-years following diagnosis.ResultsWe outline our specific strategies to estimate costs, including: defining complex research questions, defining the study population, defining initial prostate cancer treatment, linking facility and provider level related costs, and developing a shared understanding of definitions on our research team.Discussion and next stepsAnalyses are underway. We plan to include these costs in a prostate cancer patient decision aid alongside other clinical tradeoffs.

BackgroundA more accurate preoperative prediction of lymph node involvement (LNI) in prostate cancer (PCa) would improve clinical treatment and follow-up strategies of this disease. We developed a predictive model based on machine learning (ML) combined with big data to achieve this.MethodsClinicopathological characteristics of 2,884 PCa patients who underwent extended pelvic lymph node dissection (ePLND) were collected from the U.S. National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Eight variables were included to establish an ML model. Model performance was evaluated by the receiver operating characteristic (ROC) curves and calibration plots for predictive accuracy. Decision curve analysis (DCA) and cutoff values were obtained to estimate its clinical utility.ResultsThree hundred and forty-four (11.9%) patients were identified with LNI. The five most important factors were the Gleason score, T stage of disease, percentage of positive cores, tumor size, and prostate-specific antigen levels with 158, 137, 128, 113, and 88 points, respectively. The XGBoost (XGB) model showed the best predictive performance and had the highest net benefit when compared with the other algorithms, achieving an area under the curve of 0.883. With a 5%~20% cutoff value, the XGB model performed best in reducing omissions and avoiding overtreatment of patients when dealing with LNI. This model also had a lower false-negative rate and a higher percentage of ePLND was avoided. In addition, DCA showed it has the highest net benefit across the whole range of threshold probabilities.ConclusionsWe established an ML model based on big data for predicting LNI in PCa, and it could lead to a reduction of approximately 50% of ePLND cases. In addition, only ≤3% of patients were misdiagnosed with a cutoff value ranging from 5% to 20%. This promising study warrants further validation by using a larger prospective dataset.

BackgroundThe multiple imputation approach to missing data has been validated by a number of simulation studies by artificially inducing missingness on fully observed stage data under a pre-specified missing data mechanism. However, the validity of multiple imputation has not yet been assessed using real data. The objective of this study was to assess the validity of using multiple imputation for “unknown” prostate cancer stage recorded in the New South Wales Cancer Registry (NSWCR) in real-world conditions.MethodsData from the population-based cohort study NSW Prostate Cancer Care and Outcomes Study (PCOS) were linked to 2000–2002 NSWCR data. For cases with “unknown” NSWCR stage, PCOS-stage was extracted from clinical notes. Logistic regression was used to evaluate the missing at random assumption adjusted for variables from two imputation models: a basic model including NSWCR variables only and an enhanced model including the same NSWCR variables together with PCOS primary treatment. Cox regression was used to evaluate the performance of MI.ResultsOf the 1864 prostate cancer cases 32.7% were recorded as having “unknown” NSWCR stage. The missing at random assumption was satisfied when the logistic regression included the variables included in the enhanced model, but not those in the basic model only. The Cox models using data with imputed stage from either imputation model provided generally similar estimated hazard ratios but with wider confidence intervals compared with those derived from analysis of the data with PCOS-stage. However, the complete-case analysis of the data provided a considerably higher estimated hazard ratio for the low socio-economic status group and rural areas in comparison with those obtained from all other datasets.ConclusionsUsing MI to deal with “unknown” stage data recorded in a population-based cancer registry appears to provide valid estimates. We would recommend a cautious approach to the use of this method elsewhere.

PurposeTo identify the barriers and facilitators to help seeking, and screening for early presentation and diagnosis of prostate cancer in Kenya.MethodsSeven focus group discussions (FGDs) were held with a total of 46 patients diagnosed and living with prostate cancer, 65% (n = 30) from a public Hospital and 35% (n = 16) from a private hospital. An FGD guide was used to collect data on patients' barriers and facilitators of prostate cancer screening, help seeking, and diagnosis.ResultsThe patients were distributed across different age groups: 4.3 % (n = 2) were aged 50–59, 41% (n = 19) were aged 60–69, and 54% (n = 25) were aged 70 and above. The majority of the patients were in the 7th and the 8th decade 41% (n = 19) and 54% (n = 25), respectively. A larger population had at least secondary and tertiary education 39% (n = 18) and 35% (n = 16) respectively. Retired patients constituted 33% (n = 15), employed at 28% (n = 13), and 28% (n = 13) were unable to work. The main themes emerging from this study were barriers to help seeking: Lack of awareness of symptoms, symptoms misattribution and management associated stigma, impact on libido and perceptions, beliefs about prostate cancer and economic factors and financial constraints. Others were misdiagnosis, inadequate health infrastructure and failure to disclose diagnosis. Interventions emerging from the interviews were cultural sensitization and education programs, public education and awareness campaigns, positive masculinity messages and an option for gender-matched health providers.ConclusionThe delay in early presentation and diagnosis of prostate cancer is a result of the complex interplay of multiple factors. This underscores the need for a multifaceted approach to improving early presentation and diagnosis of prostate cancer. Recognizing this complexity is crucial for the development of effective holistic strategies to improve timely presentation and diagnosis and ultimately health outcomes for men at risk of prostate cancer.

Objective: To assess the extent of early mortality and its temporal course after prostatectomy and radiotherapy in the general population.Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) database and East German epidemiologic cancer registries were used for the years 2005–2013. Metastasized cases were excluded. Analyzing overall mortality, year-specific Cox regression models were used after adjusting for age (including age squared), risk stage, and grading. To estimate temporal hazards, we computed year-specific conditional hazards for surgery and radiotherapy after propensity-score matching and applied piecewise proportional hazard models.Results: In German and US populations, we observed higher initial 3-month mortality odds for prostatectomy (USA: 9.4, 95% CI: 7.8–11.2; Germany: 9.1, 95% CI: 5.1–16.2) approaching the null effect value not before 24-months (estimated annual mean 36-months in US data) after diagnosis. During the observational period, we observed a constant hazard ratio for the 24-month mortality in the US population (2005: 1.7, 95% CI: 1.5–1.9; 2013: 1.9, 95% CI: 1.6–2.2) comparing surgery and radiotherapy. The same was true in the German cohort (2005: 1.4, 95% CI: 0.9–2.1; 2013: 3.3, 95% CI: 2.2–5.1). Considering low-risk cases, the adverse surgery effect appeared stronger.Conclusion: There is strong evidence from two independent populations of a considerably higher early to midterm mortality after prostatectomy compared to radiotherapy extending the time of early mortality considered by previous studies up to 36-months.

PurposeThe risk of cardiovascular disease (CVD) mortality in patients with localized prostate cancer (PCa) by risk stratification remains unclear. The aim of this study was to determine the risk of CVD death in patients with localized PCa by risk stratification.Patients and methodsPopulation-based study of 340,806 cases in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with localized PCa between 2004 and 2016. The proportion of deaths identifies the primary cause of death, the competing risk model identifies the interaction between CVD and PCa, and the standardized mortality rate (SMR) quantifies the risk of CVD death in patients with PCa.ResultsCVD-related death was the leading cause of death in patients with localized PCa, and cumulative CVD-related death also surpassed PCa almost as soon as PCa was diagnosed in the low- and intermediate-risk groups. However, in the high-risk group, CVD surpassed PCa approximately 90 months later. Patients with localized PCa have a higher risk of CVD-related death compared to the general population and the risk increases steadily with survival (SMR = 4.8, 95% CI 4.6–5.1 to SMR = 13.6, 95% CI 12.8–14.5).ConclusionsCVD-related death is a major competing risk in patients with localized PCa, and cumulative CVD mortality increases steadily with survival time and exceeds PCa in all three stratifications (low, intermediate, and high risk). Patients with localized PCa have a higher CVD-related death than the general population. Management of patients with localized PCa requires attention to both the primary cancer and CVD.

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

This data set was collected from a group of 51 volunteers recruited from the Urologic Unit of the Bouaké University Hospital, Côte d'Ivoire. The goal was to study the correlation between dietary calcium and PSA level in a Sub-Saharan population. This is an exploratory study, among the first of its kind in Africa.

To develop a nomogram for selecting the “unreal” Gleason score (GS) 3 + 3 patients in biopsy GS 3 + 3 prostate cancer (PCa) patients. Patients who were newly diagnosed with PCa by biopsy and underwent radical prostatectomy in the First Affiliated Hospital of Nanjing Medical University from January 2009 to October 2018 were enrolled. Comparisons were made between GS 3 + 3 and higher grade PCa patients. Logistic regression analysis was performed to determine the risk factors for the “unreal” GS 3 + 3 PCa in biopsy GS 3 + 3 patients. Then, a nomogram was developed to predict the probability of “unreal” GS 3 + 3 PCa according to the results of multivariate analysis. Finally, receiver operating characteristic and decision curve analysis (DCA) curves were structured to identify the efficiency of the predictive model. Compared to higher GS grade, biopsy GS 3 + 3 had greater upgrade risk (P 

This dataset presents the footprint of cancer incidence 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 2009 to 2013 and is aggregated to Statistical Area Level 3 (SA3) geographic areas from the 2011 Australian Statistical Geography Standard (ASGS). The source of the incidence data is the 2014 Australian Cancer Database (ACD). The ACD is compiled by the Australian Institute of Health and Wellbeing (AIHW) from data provided by the state and territory population-based cancer registries.

This record contains raw data related to article "Diagnostic performance of microUltrasound at MRI-guided confirmatory biopsy in patients under active surveillance for low-risk prostate cancer"

Abstract