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.

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.

R Code, Data and Supplementary Tables to accompany paper of above title.

Snapshot img

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

Table S1: Raw data extracted from GLOBOCAN 2018 dataset for all cancers and each cancer, for all age groups and age-group intervals, for females and males, and for mortality and incidence for Arab countries, the world, USA and Europe. This data was used to generate the pivot table in Table S2 which can be queried. All data in the manuscript was based on this data file.

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.

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?

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

The Bone Cancer Treatment Market report segments the industry into Bone Cancer Type (Primary Bone Cancer, Secondary Bone Cancer (Metastatic Bone Cancer)), Treatment Type (Chemotherapy, Targeted Therapy, Radiation Therapy, Surgery, Other Treatments), and Geography (North America, Europe, Asia-Pacific, Middle-East and Africa, South America). Get five years of historical data along with five-year forecasts.

Top-ranked SNPs associated with net reproductive lifespan (p≤0.10).

Descriptive statistics by prostate cancer aggressiveness and race.

Characteristics of the 25 papers included in the review.

Additional File 5.

Study characteristics of 2,723 breast cancer cases and 3,260 controls in the MEC.