Do you know further standard populations?

If you know any further standard populations worth integrating in this dataset, please let me know in the discussion part. I would be happy to integrate further data to make this dataset more useful for everybody.

German "Federal Health Monitoring System" about 'standard populations':

"Standard populations are "artificial populations" with fictitious age structures, that are used in age standardization as uniform basis for the calculation of comparable measures for the respective reference population(s).

Use: Age standardizations based on a standard population are often used at cancer registries to compare morbidity or mortality rates. If there are different age structures in populations of different regions or in a population in one region over time, the comparability of their mortality or morbidity rates is only limited. For interregional or inter-temporal comparisons, therefore, an age standardization is necessary. For this purpose the age structure of a reference population, the so-called standard population, is assumed for the study population. The age specific mortality or morbidity rates of the study population are weighted according to the age structure of the standard population. Selection of a standard population:

Which standard population is used for comparison basically, does not matter. It is important, however, that

1. the demographic structure of the standard population is not too dissimilar to that of the reference population and
2. the comparable rates refer to the same standard."

Aim of this dataset

The aim of this dataset is to provide a variety of the most commonly used 'standard populations'.

Currently, two files with 22 standard populations are provided: - standard_populations_20_age_groups.csv - 20 age groups: '0', '01-04', '05-09', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80-84', '85-89', '90+' - 7 standard populations: 'Standard population Germany 2011', 'Standard population Germany 1987', 'Standard population of Europe 2013', 'Standard population Old Laender 1987', 'Standard population New Laender 1987', 'New standard population of Europe', 'World standard population' - source: German Federal Health Monitoring System

• standard_populations_19_age_groups.csv
  • 19 age groups: '0', '01-04', '05-09', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80-84', '85+'
  • 15 standard populations: '1940 U.S. Std Million', '1950 U.S. Std Million', '1960 U.S. Std Million', '1970 U.S. Std Million', '1980 U.S. Std Million', '1990 U.S. Std Million', '1991 Canadian Std Million', '1996 Canadian Std Million', '2000 U.S. Std Million', '2000 U.S. Std Population (Census P25-1130)', '2011 Canadian Standard Population', 'European (EU-27 plus EFTA 2011-2030) Std Million', 'European (Scandinavian 1960) Std Million', 'World (Segi 1960) Std Million', 'World (WHO 2000-2025) Std Million'
  • source: National Institutes of Health, National Cancer Institute, Surveillance, Epidemiology, and End Results Program

Terms of use

No restrictions are known to the author. Standard populations are published by different organisations for public usage.

IntroductionComparable indicators on complete cancer prevalence are increasingly needed in Europe to support survivorship care planning. Direct measures can be biased by limited registration time and estimates are needed to recover long term survivors. The completeness index method, based on incidence and survival modelling, is the standard most validated approach.MethodsWithin this framework, we consider two alternative approaches that do not require any direct modelling activity: i) empirical indices derived from long established European registries; ii) pre-calculated indices derived from US-SEER cancer registries. Relying on the EUROCARE-6 study dataset we compare standard vs alternative complete prevalence estimates using data from 62 registries in 27 countries by sex, cancer type and registration time.ResultsFor tumours mostly diagnosed in the elderly the empirical estimates differ little from standard estimates (on average less than 5% after 10-15 years of registration), especially for low prognosis cancers. For early-onset cancers (bone, brain, cervix uteri, testis, Hodgkin disease, soft tissues) the empirical method may produce substantial underestimations of complete prevalence (up to 20%) even when based on 35-year observations. SEER estimates are comparable to the standard ones for most cancers, including many early-onset tumours, even when derived from short time series (10-15 years). Longer observations are however needed when cancer-specific incidence and prognosis differ remarkably between US and European populations (endometrium, thyroid or stomach).DiscussionThese results may facilitate the dissemination of complete prevalence estimates across Europe and help bridge the current information gaps.

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Cancer Registry Software Market Size 2025-2029

The cancer registry software market size is forecast to increase by USD 121.9 million, at a CAGR of 14% between 2024 and 2029.

The market is witnessing significant growth due to the escalating prevalence of cancer cases worldwide. The increasing incidence of various types of cancer necessitates the implementation of advanced registry software solutions to manage and analyze patient data more efficiently. Moreover, the burgeoning clinical research in oncology further drives the demand for these systems, as they facilitate data collection, management, and analysis for research purposes. However, the market faces challenges in the form of stringent data privacy and security concerns. With the growing amount of sensitive patient information being stored and shared digitally, ensuring robust data security becomes crucial. The potential risks of data breaches and unauthorized access can significantly impact both patients and healthcare providers, necessitating the adoption of advanced security measures. Companies in the market must prioritize data security and privacy to gain the trust of healthcare organizations and patients alike.

What will be the Size of the Cancer Registry Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market is a dynamic and evolving landscape, continually adapting to advancements in healthcare technology and the growing demand for comprehensive cancer data management. This market encompasses various applications, including disease registry management, cancer staging system, data warehousing, cancer incidence tracking, registry software architecture, data integration platform, clinical data capture, case reporting system, statistical reporting, cancer screening programs, and more. These tools play a crucial role in cancer surveillance systems, enabling the collection, analysis, and reporting of epidemiological data for public health surveillance. They facilitate data encryption for patient data privacy, ensuring HIPAA compliance. Data interoperability and data quality metrics are essential components, allowing for seamless integration of various health informatics tools. Real-time data updates and database management systems are integral to maintaining accurate and up-to-date information. Predictive modeling tools and data mining techniques contribute to risk factor identification and mortality data analysis. Data visualization tools offer valuable insights into the complexities of cancer data. Cancer registry software architecture supports population-based registry initiatives, ensuring secure data storage and registry reporting features. Oncology data management tools enable clinical data capture, case reporting, and statistical reporting, enhancing overall patient care. The ongoing development and refinement of these tools reflect the continuous unfolding of market activities and evolving patterns in cancer data management.

How is this Cancer Registry Software Industry segmented?

The cancer registry software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userGovernment and third partyPharma biotech and medical device companiesHospitals and medical practicePrivate payersResearch institutesTypeStand-alone softwareIntegrated softwareDeploymentOn-premisesCloud-basedGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalySpainUKAPACChinaJapanRest of World (ROW)

By End-user Insights

The government and third party segment is estimated to witness significant growth during the forecast period.Cancer registry software solutions play a vital role in assisting government and third-party agencies in managing and analyzing data related to cancer cases. These systems enable the tracking of cancer incidence, prevalence, and mortality rates, providing essential information for public health planning, resource allocation, and policy development. Analyzing trends and patterns in registry data helps identify high-risk populations, geographic disparities, and emerging cancer types. Governments utilize cancer registry software to monitor and improve the quality of cancer care. By evaluating variations in treatment practices and adherence to clinical guidelines, they can benchmark outcomes against national or international standards. Additionally, these software solutions facilitate data interoperability, ensuring data quality metrics and HIPAA compliance. Data encryption, data visualization tools, and predictive modeling capabilities enhance the functionality of cancer registry software. Epidemiological data analysis and risk factor identificatio

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

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Welcome to our regular closed dataset based weekend hackathon. In this weekend hackathon, we are challenging all the machinehackers to predict the melanoma tumor size based on various attributes. Melanomas present in many different shapes, sizes, and colors. That’s why it’s tricky to provide a comprehensive set of warning signs. Melanoma, also known as malignant melanoma, is a type of skin cancer that develops from the pigment-producing cells known as melanocytes. The primary cause of melanoma is ultraviolet light (UV) exposure in those with low levels of the skin pigment melanin. The UV light may be from the sun or other sources, such as tanning devices.

Melanoma is the most dangerous type of skin cancer. Globally, in 2012, it newly occurred in 232,000 people. In 2015, there were 3.1 million people with active disease, which resulted in 59,800 deaths. Australia and New Zealand have the highest rates of melanoma in the world. There are also high rates in Northern Europe and North America, while it is less common in Asia, Africa, and Latin America. In the United States melanoma occurs about 1.6 times more often in men than women.

Data Description:

Train.csv - 9146 rows x 9 columns
Test.csv - 36584 rows x 8 columns
Sample Submission - Acceptable submission format 

Attributes Description:

mass_npea: the mass of the area understudy for melanoma tumor
size_npear: the size of the area understudy for melanoma tumor
malign_ratio: ration of normal to malign surface understudy
damage_size: unrecoverable area of skin damaged by the tumor
exposed_area: total area exposed to the tumor
std_dev_malign: standard deviation of malign skin measurements
err_malign: error in malign skin measurements
malign_penalty: penalty applied due to measurement error in the lab
damage_ratio: the ratio of damage to total spread on the skin
tumor_size: size of melanoma_tumor

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

The incidence and mortality of colorectal cancer (CRC) is higher in African Americans (AAs) than in other ethnic groups in the U. S., but reasons for the disparities are unknown. We performed gene expression profiling and microsatellite instability (MSI) analysis of sporadic CRCs from AAs vs. European Americans (EAs) to assess the contribution to CRC disparities. We evaluated gene expression of 43 AA and 43 EA CRC tumors matched by stage and 40 normal colon tissues using the Agilent human whole genome 4x44K cDNA arrays. Gene and pathway analysis were performed using Significance Analysis of Microarrays (SAM), 10-fold Cross Validation (10-fCV) and Ingenuity Pathway Analysis (IPA). MSI analysis was assessed with five NIH Bethesda markers. SAM revealed that 95 genes were differentially expressed between AA and EA patients at a false discovery rate of <5%. A 10f-CV demonstrated that 9 genes were differentially expressed between AA and EA with an accuracy of 97%. Nine genes (CRYBB2, PSPH, ADAL, VSIG10L, C17orf81, ARSE, ANKRD36B, ZNF835, ARHGAP6) were validated and differential expression confirmed by qRT-PCR in independent test set of 21 patients (10 AA, 11 EA). We also analyzed MSI in 57 of the CRC subjects. Overall, 15.8% of CRC patients had MSI, with a higher rate observed in EA (20%) than in AA (12%). MSI distribution by tumor site was 77% right and 23% left colon. Previously, genetic, epigenetic and environmental factors have been implicated in the etiology of CRC. Our results are the first to implicate differential gene expression in CRC disparities and support the existence of distinct tumor microenvironments in these two patients' populations. Overall design: 126 total samples: 1) 43 white cancer samples; 2) 43 black cancer samples; 3) 27 white control samples; 4) 13 black control samples.

Optical Coherence Tomography (OCT) Image dataset of radiation dermatitis

Citing the Dataset

The dataset is released under a Creative Commons Attribution license, so please cite the dataset if it is used in your work in any form. Published academic papers should use the academic paper citation for our paper. Personal works, such as projects or blog posts, should provide a URL to this Zenodo page, though a reference to our paper would also be appreciated.

Academic paper citation

Photiou C., Cloconi C. & Strouthos I. Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01241-4

Personal use citation

Include a link to this Zenodo page - 10.5281/zenodo.8238140

ACKNOWLEDGMENT

This research is funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for European Programs, Coordination and Development.

Contact Information

If you would like further information about the dataset, or if you experience any issues downloading files, please contact us at photiou.christos@ucy.ac.cy.

Dataset Description

This dataset consists of Optical Coherence Tomography (OCT) images from 22 head and neck cancer patients undergoing radiotherapy. Specifically, this dataset includes OCT images of five stages of Acute Radiation Dermatitis (ARD), labelled by an expert oncologist as Grade 0 (0), Grade 1 (1), Grade 2a (2), Grade 2b (3) and Grade 3 (4). Twenty-two head and neck cancer patients who were scheduled to receive radiation therapy at the German Oncology Center (GOC) in Limassol, Cyprus, participated in this proof-of-concept trial. The trial has received bioethics approval from the Cyprus National Bioethics Committee (Cyprus National Bioethics Committee 2020/61) and informed consent was collected. Patients under the age of 18 or with disabilities, expectant women, those who had recently undergone radiation therapy in the same area, and patients with autoimmune diseases were excluded from the study. After informed consent, the irradiated side of the neck of the subjects, was imaged with OCT. The imaging was performed with a swept-source OCT system (Santec IVS300), with a center wavelength of 1300 nm, an axial resolution of 12 micrometers in tissue, and an A-scan rate of 40 kHz. Six images were acquired at 1 cm intervals, covering the region from the mandibular angle to the clavicle. Imaging was repeated prior to every radiation therapy session, twice per week, until the conclusion of the therapy, resulting in a dataset of 1487 images. During each visit, the patient's ARD grade, at each of the imaging sites, was determined and recorded by a senior oncologist.

Dataset
The data consists of two items: (1) the excel file 'Description.xlsx' with the patient information and (2) the zip file 'Dataset.zip' containing the images, as described below.

1) Description.xlsx
This excel file contains patient information such as age, habits, etc, in the sheet 'Patient_Info'. The sheet 'Image_Info' contains the information for each image, such as the patient number (1-22), week number, visit number (usually one or two visits per week), image number (six images per visit with some exceptions), and classification (0-4).

2) Dataset.zip
This zip file contains the OCT images. Each patient's folder has sub-folders corresponding to each week, within which there are sub-folders corresponding to each visit, which contain the image folders. Each image folder contains two excel files: OCT Data (demodulated and logarithmic intensity image) and Raw Data (resampled interferometric data).

Context

Welcome to our regular closed dataset based weekend hackathon. In this weekend hackathon, we are challenging all the machinehackers to predict the melanoma tumor size based on various attributes. Melanomas present in many different shapes, sizes, and colors. That’s why it’s tricky to provide a comprehensive set of warning signs. Melanoma, also known as malignant melanoma, is a type of skin cancer that develops from the pigment-producing cells known as melanocytes. The primary cause of melanoma is ultraviolet light (UV) exposure in those with low levels of the skin pigment melanin. The UV light may be from the sun or other sources, such as tanning devices.

Melanoma is the most dangerous type of skin cancer. Globally, in 2012, it newly occurred in 232,000 people. In 2015, there were 3.1 million people with active disease, which resulted in 59,800 deaths. Australia and New Zealand have the highest rates of melanoma in the world. There are also high rates in Northern Europe and North America, while it is less common in Asia, Africa, and Latin America. In the United States melanoma occurs about 1.6 times more often in men than women.

Content

Train.csv - 9146 rows x 9 columns Test.csv - 36584 rows x 8 columns Sample Submission - Acceptable submission format

Attributes

Acknowledgements

Machine Hack: Melanoma Tumor Size Prediction

BackgroundEsophageal cancer remains one of the deadliest cancers globally, highlighting significant health challenges and socioeconomic disparities. This study aims to measure its global burden, assess disparities by sex, age, and region, and evaluate health inequalities, with projections to 2050. The goal is to provide evidence to guide resource allocation and reduce the disease burden.MethodsUsing data from the Global Burden of Disease (GBD) 2021 study, we analyzed trends in prevalence, incidence, mortality, and Disability-Adjusted Life Years (DALYs) across sexes, age groups, and 204 countries and territories. Age-standardized rates (ASR) were calculated to account for population age structures. Trends over time were assessed using the estimated annual percentage change (EAPC). Health inequalities were evaluated using the Slope Index of Inequality (SII) and Concentration Index (CI). Future burdens were projected using Bayesian Age-Period-Cohort (BAPC) models.ResultsFrom 1990 to 2021, esophageal cancer cases increased: prevalence from 551.62 to 1004.2 thousand, incidence from 354.73 to 576.53 thousand, mortality from 356.26 to 538.6 thousand, and DALYs from 9753.57 to 12999.26 thousand. However, age-standardized rates declined: prevalence from 13.34 to 11.47, incidence from 8.86 to 6.65, mortality from 9.02 to 6.25, and DALYs from 235.32 to 148.56 per 100,000 people. The burden rises sharply after age 40, with males and low-SDI regions experiencing higher burdens. Health inequalities widened, with the SII for prevalence increasing from 2.52 to 5.67, and for deaths from 1.45 to 2.94. West Africa, North Europe, and North America saw rising prevalence rates, while East Asia showed a declining trend. A decreasing trend is observed in most countries and regions worldwide, particularly in East Asia, with projections suggesting a continued decline in the future.ConclusionAlthough projections indicate a decreasing trend, health inequalities have intensified. Regions such as West Africa, North Europe, and North America are experiencing rising prevalence. To address these disparities, targeted interventions, enhanced healthcare access, and preventive measures in high-burden areas are essential to reduce the global burden and advance health equity.

Descriptive statistics by prostate cancer aggressiveness and race.

The death cases and age-standardized mortality rates from pancreatic cancer, as well as their temporal trends by sex and super-region from 1990 to 2019.