This dataset contains the raw files exported from the CDC-WONDER database, and selections needed on the CDC-WONDER Multiple Causes of Death database in order to access and replicate our data and findings.

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

Dataset Description This dataset contains information on cancer deaths by country, type, and year. It includes data on 18 different types of cancer, including liver cancer, kidney cancer, larynx cancer, breast cancer, thyroid cancer, stomach cancer, bladder cancer, uterine cancer, ovarian cancer, cervical cancer, prostate cancer, pancreatic cancer, esophageal cancer, testicular cancer, nasopharynx cancer, other pharynx cancer, colon and rectum cancer, non-melanoma skin cancer, lip and oral cavity cancer, brain and nervous system cancer, tracheal, bronchus, and lung cancer, gallbladder and biliary tract cancer, malignant skin melanoma, leukemia, Hodgkin lymphoma, multiple myeloma, and other cancers.

Data Fields The dataset includes the following data fields:

• Country: The country where the cancer death occurred.
• Code: The country code for the country where the cancer death occurred.
• Year: The year in which the cancer death occurred.
• Liver cancer: The number of cancer deaths from liver cancer in the country in the year.
• Kidney cancer: The number of cancer deaths from kidney cancer in the country in the year.
• Larynx cancer: The number of cancer deaths from larynx cancer in the country in the year.
• Breast cancer: The number of cancer deaths from breast cancer in the country in the year.
• Thyroid cancer: The number of cancer deaths from thyroid cancer in the country in the year.
• Stomach cancer: The number of cancer deaths from stomach cancer in the country in the year.
• Bladder cancer: The number of cancer deaths from bladder cancer in the country in the year.
• Uterine cancer: The number of cancer deaths from uterine cancer in the country in the year.
• Ovarian cancer: The number of cancer deaths from ovarian cancer in the country in the year.
• Cervical cancer: The number of cancer deaths from cervical cancer in the country in the year.
• Prostate cancer: The number of cancer deaths from prostate cancer in the country in the year.
• Pancreatic cancer: The number of cancer deaths from pancreatic cancer in the country in the year.
• Esophageal cancer: The number of cancer deaths from esophageal cancer in the country in the year.
• Testicular cancer: The number of cancer deaths from testicular cancer in the country in the year.
• Nasopharynx cancer: The number of cancer deaths from nasopharynx cancer in the country in the year.
• Other pharynx cancer: The number of cancer deaths from other pharynx cancer in the country in the year.
• Colon and rectum cancer: The number of cancer deaths from colon and rectum cancer in the country in the year.
• Non-melanoma skin cancer: The number of cancer deaths from non-melanoma skin cancer in the country in the year.
• Lip and oral cavity cancer: The number of cancer deaths from lip and oral cavity cancer in the country in the year.
• Brain and nervous system cancer: The number of cancer deaths from brain and nervous system cancer in the country in the year.
• Tracheal, bronchus, and lung cancer: The number of cancer deaths from tracheal, bronchus, and lung cancer in the country in the year.
• Gallbladder and biliary tract cancer: The number of cancer deaths from gallbladder and biliary tract cancer in the country in the year.
• Malignant skin melanoma: The number of cancer deaths from malignant skin melanoma in the country in the year.
• Leukemia: The number of cancer deaths from leukemia in the country in the year.
• Hodgkin lymphoma: The number of cancer deaths from Hodgkin lymphoma in the country in the year.
• Multiple myeloma: The number of cancer deaths from multiple myeloma in the country in the year.
• Other cancers: The number of cancer deaths from other cancers in the country in the year.

Data Source The data in this dataset was collected from the World Health Organization (WHO). The WHO collects data on cancer deaths from countries around the world.

Usage This dataset can be used to study cancer deaths by country, type, and year. It can also be used to compare cancer death rates between different countries or over time.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16169071%2F98f6c6f321aad496b703685519b6df6a%2Fcancer-cells-th.jpg?generation=1694610742970317&alt=media" alt="">

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

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.

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

** 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)

This dataset contains Cancer Incidence data for Prostate 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 2018 to 2022.Data are for males segmented 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 (SEER areas use 20 age groups and NPCR areas use 19 age groups). 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. Due to changes in stage coding, Combined Summary Stage with Expanded Regional Codes (2004+) is used for data from Surveillance, Epidemiology, and End Results (SEER) databases and Merged Summary Stage is used for data from National Program of Cancer Registries databases. Due to the increased complexity with staging, other staging variables maybe used if necessary.Data Source Field Key(2) 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 2024 submission).(7) Source: SEER November 2024 submission.

Summary of publications used as data sources for the clinical states model (Table A). Prevalence of clinical states, incidence flow, and patient flows between the clinical states for each year from 2010 to 2020 (Table B). Incidence of prostate cancer in the United States between 1990 and 2009. Grouped by clinical state at the time of diagnosis according to the Surveillance Epidemiology and End Results database (Figure A). Annual all-cause mortality by clinical state, base-case model in 2009 (Figure B). (DOCX)

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.

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.

PROSTATEx has been superseded by PI-CAI:

Accessing the PROSTATEx Challenge Data Sets

The PROSTATEx Challenge ("SPIE-AAPM-NCI Prostate MR Classification Challenge”) focused on quantitative image analysis methods for the diagnostic classification of clinically significant prostate cancers and was held in conjunction with the 2017 SPIE Medical Imaging Symposium. PROSTATEx ran from November 21, 2016 to January 15, 2017, though a "live" version has also been established at https://prostatex.grand-challenge.org which serves as an ongoing way for researchers to benchmark their performance for this task.

The PROSTATEx-2 Challenge ("SPIE-AAPM-NCI Prostate MR Gleason Grade Group Challenge" ) ran from May 15, 2017 to June 23, 2017 and was focused on the development of quantitative multi-parametric MRI biomarkers for the determination of Gleason Grade Group in prostate cancer. It was held in conjunction with the 2017 AAPM Annual Meeting (see http://www.aapm.org/GrandChallenge/PROSTATEx-2).

Supplemental data and instructions specific to both challenges are in the Detailed Description section below.

This collection is a retrospective set of prostate MR studies. All studies included T2-weighted (T2W), proton density-weighted (PD-W), dynamic contrast enhanced (DCE), and diffusion-weighted (DW) imaging. The images were acquired on two different types of Siemens 3T MR scanners, the MAGNETOM Trio and Skyra. T2-weighted images were acquired using a turbo spin echo sequence and had a resolution of around 0.5 mm in plane and a slice thickness of 3.6 mm. The DCE time series was acquired using a 3-D turbo flash gradient echo sequence with a resolution of around 1.5 mm in-plane, a slice thickness of 4 mm and a temporal resolution of 3.5 s. The proton density weighted image was acquired prior to the DCE time series using the same sequence with different echo and repetition times and a different flip angle. Finally, the DWI series were acquired with a single-shot echo planar imaging sequence with a resolution of 2 mm in-plane and 3.6 mm slice thickness and with diffusion-encoding gradients in three directions. Three b-values were acquired (50, 400, and 800), and subsequently, the ADC map was calculated by the scanner software. All images were acquired without an endorectal coil.

Number and rate of new cancer cases diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

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.

BackgroundWith the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including prostate cancer. Facts have proved that AI has broad prospects in the accurate diagnosis and treatment of prostate cancer.ObjectiveThis study mainly summarizes the research on the application of artificial intelligence in the field of prostate cancer through bibliometric analysis and explores possible future research hotspots.MethodsThe articles and reviews regarding application of AI in prostate cancer between 1999 and 2020 were selected from Web of Science Core Collection on August 23, 2021. Microsoft Excel 2019 and GraphPad Prism 8 were applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 5.8.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field.ResultsA total of 2,749 articles were selected in this study. AI-related research on prostate cancer increased exponentially in recent years, of which the USA was the most productive country with 1,342 publications, and had close cooperation with many countries. The most productive institution and researcher were the Henry Ford Health System and Tewari. However, the cooperation among most institutions or researchers was not close even if the high research outputs. The result of keyword analysis could divide all studies into three clusters: “Diagnosis and Prediction AI-related study”, “Non-surgery AI-related study”, and “Surgery AI-related study”. Meanwhile, the current research hotspots were “deep learning” and “multiparametric MRI”.ConclusionsArtificial intelligence has broad application prospects in prostate cancer, and a growing number of scholars are devoted to AI-related research on prostate cancer. Meanwhile, the cooperation among various countries and institutions needs to be strengthened in the future. It can be projected that noninvasive diagnosis and accurate minimally invasive treatment through deep learning technology will still be the research focus in the next few years.

BackgroundGlobally, the incidence of prostate cancer is increasing, particularly in low- and middle-income countries. It is the most common cancer among men worldwide, with higher mortality in low and middle-income countries. In Ethiopia, it is the second most common cause of cancer morbidity and mortality among men. Despite a few studies done regarding the disease burden, the evidence is scarce about the survival and prognostic determinants of prostate cancer patients in Ethiopia. Thus, this study assessed the survival and prognostic determinants of patients with prostate cancer.MethodsWe retrospectively followed patients who were newly diagnosed from 2012 to 2016 at the Oncology Department of Tikur Anbessa Specialized Hospital. We extracted the data from patient charts that were available in the cancer registry using a checklist with the help of oncology nurses. Kaplan-Meier survival analyses with the log-rank test were used to estimate and compare the probability of survival among covariate categories. After checking for assumptions, a multivariable Cox regression analysis was performed to identify prognostic determinants of survival.ResultsThe median survival time was 28 months with an overall 2-, 3- and 5-year survival of 57%, 38.9% and 22%, respectively. The overall survival differs according to the clinical stage (P-value<0.01), presence or absence of distant metastasis (P<0.01) and androgen deprivation therapy (ADT) (P<0.05). Cancer stage at diagnosis (adjusted hazard ratio (AHR) = 0.309, 95%CI = 0.151–0.633) and ADT (AHR = 3.884, 95%CI = 1.677–8.997) remained significant in the final Cox proportional hazards model.ConclusionsThe overall 2-, 3- and 5-year survival of prostate cancer patients in Ethiopia is very low. The cancer stage at diagnosis and treatment modalities are significant prognostic determinants of survival. Therefore, early detection through screening and timely initiation of treatment are essential to improve the survival of prostate cancer patients.

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 <0.001). We also compared cancer mortality rates among foreign-born (13.9%) and Sweden-born (86.1%) individuals and determined the effect of education level and sex estimated by mortality rate ratios (MRRs) using multivariable Poisson regression. All-site cancer mortality was slightly higher among foreign-born than Sweden-born men (MRR = 1.05, 95% confidence interval 1.04–1.07), but similar mortality risks was found among foreign-born and Sweden-born women. Men born in Angola, Laos, and Cambodia had the highest cancer mortality risk. Women born in all countries except Iceland, Denmark, and Mexico had a similar or smaller risk than women born in Sweden. Cancer-specific mortality analysis showed an increased risk for cervical and lung cancer in both sexes but a decreased risk for colon, breast, and prostate cancer mortality among foreign-born compared with Sweden-born individuals. Further studies are required to fully understand the causes of the observed inequalities in mortality across levels of education and countries of birth.

This dataset contains 600 entries with 9 attributes related to prostate cancer diagnosis. The dataset includes measurements of tumor characteristics such as radius, texture, perimeter, area, smoothness, compactness, symmetry, and fractal dimension. These numerical features help in identifying the nature of the tumor. The target variable, "diagnosis_result", is categorical and has two classes: - M (Malignant) – Cancerous tumor - B (Benign) – Non-cancerous tumor This dataset can be used for predictive modeling to classify prostate cancer tumors using machine learning algorithms.

1. radius – The average distance from the center to the tumor boundary. A larger radius may indicate a larger tumor size – float data type
2. texture – Variation in intensity levels within the tumor image. It helps in identifying structural differences in the tissue – float data type
3. perimeter – The length of the tumor boundary. A higher value can be associated with an irregular or larger tumor shape – float data type
4. area– The total size of the tumor. A larger area may indicate a more advanced stage of cancer – float data type
5. smoothness– Measures how smooth or irregular the tumor surface is. Lower values suggest a smoother boundary, while higher values indicate irregular growth – float data type
6. compactness – A ratio that compares the perimeter and area of the tumor. Higher compactness may indicate a denser, more aggressive tumor – float data type
7. symmetry– Evaluates how symmetrical the tumor shape is. Asymmetry can be a sign of malignancy – float data type
8. fractal_dimension – A mathematical measure of tumor complexity. Higher values indicate a more complex tumor structure – float data type
9. diagnosis_result– The classification label indicating whether the tumor is Malignant (M) (cancerous) or Benign (B) (non-cancerous) – object (categorical: 'M' or 'B') data type

This dataset was derived from tracked biopsy sessions using the Artemis biopsy system, many of which included image fusion with MRI targets. Patients received a 3D transrectal ultrasound scan, after which nonrigid registration (e.g. “fusion”) was performed between real-time ultrasound and preoperative MRI, enabling biopsy cores to be sampled from MR regions of interest. Most cases also included sampling of systematic biopsy cores using a 12-core digital template. The Artemis system tracked targeted and systematic core locations using encoder kinematics of a mechanical arm, and recorded locations relative to the Ultrasound scan. MRI biopsy coordinates were also recorded for most cases. STL files and biopsy overlays are available and can be visualized in 3D Slicer with the SlicerHeart extension. Spreadsheets summarizing biopsy and MR target data are also available. See the Detailed Description tab below for more information.

MRI targets were defined using multiparametric MRI, e.g. t2-weighted, diffusion-weighted, and perfusion-weighted sequences, and scored on a Likert-like scale with close correspondence to PIRADS version 2. t2-weighted MRI was used to trace ROI contours, and is the only sequence provided in this dataset. MR imaging was performed on a 3 Tesla Trio, Verio or Skyra scanner (Siemens, Erlangen, Germany). A transabdominal phased array was used in all cases, and an endorectal coil was used in a subset of cases. The majority of pulse sequences are 3D T2:SPC, with TR/TE 2200/203, Matrix/FOV 256 × 205/14 × 14 cm, and 1.5mm slice spacing. Some cases were instead 3D T2:TSE with TR/TE 3800–5040/101, and a small minority were imported from other institutions (various T2 protocols.)

Ultrasound scans were performed with Hitachi Hi-Vision 5500 7.5 MHz or the Noblus C41V 2-10 MHz end-fire probe. 3D scans were acquired by rotation of the end-fire probe 200 degrees about its axis, and interpolating to resample the volume with isotropic resolution.

Patients with suspicion of prostate cancer due to elevated PSA and/or suspicious imaging findings were consecutively accrued. Any consented patient who underwent or had planned to receive a routine, standard-of-care prostate biopsy at the UCLA Clark Urology Center was included.

Note: Some Private Tags in this collection are critical to properly displaying the STL surface and the Prostate anatomy. Private Tag (1129,"Eigen, Inc",1016) DS VoxelSize is especially important for multi-frame US cases.

PurposeAlthough the adverse effects of atrial fibrillation (AF) on cancers have been well reported, the relationship between the AF and the adverse outcomes in prostate cancer (PC) remains inconclusive. This study aimed to explore the prevalence of AF and evaluate the relationship between AF and clinical outcomes in PC patients.MethodsPatients diagnosed with PC between 2008 and 2017 were identified from the National Inpatient Sample database. The trends in AF prevalence were compared among PC patients and their subgroups. Multivariable regression models were used to assess the associations between AF and in-hospital mortality, length of hospital stay, total cost, and other clinical outcomes.Results256,239 PC hospitalizations were identified; 41,356 (83.8%) had no AF and 214,883 (16.2%) had AF. AF prevalence increased from 14.0% in 2008 to 20.1% in 2017 (P < .001). In-hospital mortality in PC inpatients with AF increased from 5.1% in 2008 to 8.1% in 2017 (P < .001). AF was associated with adverse clinical outcomes, such as in-hospital mortality, congestive heart failure, pulmonary circulation disorders, renal failure, fluid and electrolyte disorders, cardiogenic shock, higher total cost, and longer length of hospital stay.ConclusionsThe prevalence of AF among inpatients with PC increased from 2008 to 2017. AF was associated with poor prognosis and higher health resource utilization. Better management strategies for patients with comorbid PC and AF, particularly in older individuals, are required.