Population based cancer incidence rates were abstracted from National Cancer Institute, State Cancer Profiles for all available counties in the United States for which data were available. This is a national county-level database of cancer data that are collected by state public health surveillance systems. All-site cancer is defined as any type of cancer that is captured in the state registry data, though non-melanoma skin cancer is not included. All-site age-adjusted cancer incidence rates were abstracted separately for males and females. County-level annual age-adjusted all-site cancer incidence rates for years 2006–2010 were available for 2687 of 3142 (85.5%) counties in the U.S. Counties for which there are fewer than 16 reported cases in a specific area-sex-race category are suppressed to ensure confidentiality and stability of rate estimates; this accounted for 14 counties in our study. Two states, Kansas and Virginia, do not provide data because of state legislation and regulations which prohibit the release of county level data to outside entities. Data from Michigan does not include cases diagnosed in other states because data exchange agreements prohibit the release of data to third parties. Finally, state data is not available for three states, Minnesota, Ohio, and Washington. The age-adjusted average annual incidence rate for all counties was 453.7 per 100,000 persons. We selected 2006–2010 as it is subsequent in time to the EQI exposure data which was constructed to represent the years 2000–2005. We also gathered data for the three leading causes of cancer for males (lung, prostate, and colorectal) and females (lung, breast, and colorectal). The EQI was used as an exposure metric as an indicator of cumulative environmental exposures at the county-level representing the period 2000 to 2005. A complete description of the datasets used in the EQI are provided in Lobdell et al. and methods used for index construction are described by Messer et al. The EQI was developed for the period 2000– 2005 because it was the time period for which the most recent data were available when index construction was initiated. The EQI includes variables representing each of the environmental domains. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., L. Messer, K. Rappazzo , C. Gray, S. Grabich , and D. Lobdell. County-level environmental quality and associations with cancer incidence#. Cancer. John Wiley & Sons Incorporated, New York, NY, USA, 123(15): 2901-2908, (2017).

United States Burden of Melanoma and Non-Melanoma Skin Cancer from 1990 to 2019 Supplementary Figures and Tables

Rate: Number of deaths due melanoma cancer per 100,000 Population.

Definition: Number of deaths per 100,000 with malignant melanoma of the skin as the underlying cause of death (ICD-10 code: C43).

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

Although skin cancers are considered mostly preventable, more people in the US are diagnosed with skin cancer than all other cancers combined. Sun safety recommendations include wearing sun-protective clothing, a wide-brimmed hat, seeking shade, and using sunscreen. Some evidence exists that sun risk behaviors and skin cancer rates are more frequent among rural than urban US populations, raising questions about underlying factors. We conducted a belief elicitation survey on these four sun protection behaviors among 278 adults (aged 18–60 years) living in rural Minnesota, a state with high sunburn rates and UV-attributable melanoma cases. These qualitative data were analyzed using content analysis, and the identified codes ranked by frequency. Almost all participants emphasized that spending time outside was important to them. The most frequently reported sun protection behaviors were wearing sunscreen and protective clothing. The primary outcomes were obtained from open-ended questions on outcome, normative, and control beliefs associated with each sun protection behavior. While many different beliefs were mentioned, reducing sunburn and skin cancer risk were commonly reported across all behaviors. Beliefs about negative aspects of each behavior (e.g., interference with being physically active or doing work outside, greasy/sticky sunscreen, not getting a suntan, overheating in long clothes or when wearing hats, hats that blow off easily) typically outnumbered positive aspects (e.g., protective behaviors enabling being outside, staying cool in shade, reduced skin aging). The majority of participants believed that most people would approve of all protection behaviors, but many thought that age was a factor for behavior adoption, with young people typically thought to engage less in protective behaviors. Some commonly reported negative aspects of sun protective behaviors were related to activities more common in rural populations, such as working outside. This suggests that rural sun protection promotion may include structural interventions to make sun protection easy, convenient, and accessible without impeding rural lifestyles.

Supplemental material for our study "Incidence of melanoma and its subtypes by census tract-level socioeconomic status: a United States population-based cohort analysis"

ABSTRACT Objectives: to evaluate the profile of skin cancer in Pomeranian communities of the State of Espírito Santo, composed of descendants of European immigrants, regarding gender and age at diagnosis, lesion size and histological type. Method: we studied histopathological reports of 3,781 patients operated between 2000 and 2010, with resection of 4,881 lesions. We assessed histological type, lesion size, age and gender of the patients at diagnosis and their correlations in the 11-year period. Results: the histopathological examination revealed basal cell carcinoma in 3,159 patients (83.5%), squamous cell carcinoma in 415 (11%), melanoma in 64 (1.7%), and 143 patients (3.8%) had combined lesions of basal cell carcinoma and squamous cell carcinoma. As to size, 47.1% measured between 5.1 and 10mm. The age group of 61 to 70 years was the one that sustained the largest number of surgical interventions (24.3%). There was a predominance of the female gender (2,027, 53.6%) in relation to the male (1,754, 46.4%). Conclusion: basal cell carcinoma was the most frequent histological type. The prevalences of squamous cell carcinoma and melanoma were below the national estimate of the National Cancer Institute. The diagnosis of tumors occurred at more advanced ages (above 60 years) and there was an increase in the incidence and size of skin tumors in the male population.

Melanoma, a lethal form of skin cancer, poses a significant health risk worldwide with rising incident rates. The usage of Artificial Intelligence (AI) tools in dermatology for melanoma detection can help curb the demand for accurate and efficient diagnosis of the disease. This review examines the current state of AI, Machine Learning (ML), and Deep Learning (DL) applications in the identification of melanomas through the analysis of various studies that have demonstrated the potential of these technologies that could outperform traditional methods and provide life-saving diagnoses. The primary usage of Convolutional Neural Networks (CNNs) has the potential to completely revolutionize the field of dermatological diagnosis.

This publication reports on newly diagnosed cancers registered in England in addition to cancer deaths registered in England during 2020. It includes this summary report showing key findings, spreadsheet tables with more detailed estimates, and a methodology document.

Melanoma is one of the deadliest forms of skin cancer but is typically cured with surgical excision when detected early. As an access point to medical care, primary care providers (PCP) play an integral role in early skin cancer detection. However, limited time for examinations and dermatologic training may present barriers to effective skin examination in the primary care setting. As a facet of Oregon Health & Science University’s War on Melanoma™ (WoM), our multi-pronged outreach initiative aims to provide PCPs across Oregon with free, convenient, and effective melanoma education. The WoM PCP education campaign was disseminated starting in May 2019 through primary care networks throughout the state of Oregon to 12,792 PCPs, and education was delivered across several platforms: online multimedia tools, large group didactics, individualized practice-based sessions, and in-person distribution of materials to clinics. To date, 829 PCPs have participated in the online Melanoma Toolkit for Early Detection curriculum, 1,874 providers have attended CME didactics, and 9 clinics have received facilitated meetings by Oregon Rural Practice-based Research Network. Eighty-three clinics (comprising 770 providers) were visited on-site and provided educational materials, and more than 150 PCPs have received a free smartphone dermatoscope to aid in skin examination and e-consultation. OHSU’s WoM has successfully implemented a multifaceted approach to provide accessible melanoma education to PCPs across the state of Oregon. As a result, we hope to encourage appropriate skin examination in the primary care setting and improve PCPs’ diagnostic accuracy and confidence in pigmented lesion evaluation.

The ISIC 2017 Skin Lesion Analysis Dataset, available on Kaggle, is a benchmark dataset primarily used for skin cancer classification and segmentation tasks, focusing on melanoma detection. It is part of the International Skin Imaging Collaboration (ISIC) archive, a global effort to improve melanoma diagnosis through machine learning.

Dataset Details Content:

Images of dermoscopic skin lesions. Accompanying metadata for each image (patient age, gender, lesion localization, etc.). Ground truth segmentation masks for the lesions. Labels for classification tasks: "Benign" and "Malignant." Number of Images:

Approximately 2000 dermoscopic images. Split into training, validation, and test sets for easy use. Tasks:

Classification: Predict whether a lesion is benign or malignant. Segmentation: Identify and segment the lesion in an image. Image Specifications:

High-resolution dermoscopic images, captured under standardized conditions. Images are RGB format, typically ranging in size between 500x500 and 1024x1024 pixels. Annotation Quality:

The dataset is curated and annotated by dermatology experts, ensuring high-quality and reliable labels. Applications Training machine learning models for early melanoma detection. Research on automated lesion segmentation and classification. Benchmarking new algorithms against state-of-the-art performance. Why is it Important? Melanoma is a severe form of skin cancer, and early detection is critical for effective treatment. The ISIC dataset provides a reliable resource for developing AI tools to assist dermatologists and improve diagnostic accuracy.

Challenges High class imbalance: Malignant cases are fewer compared to benign. Variability in lesion appearance due to factors like skin tone, lighting, and lesion morphology. Using the Dataset The dataset can be accessed on Kaggle, often requiring agreement to terms of use, as it contains medical data. It's suitable for experiments using deep learning frameworks like TensorFlow or PyTorch.

References The official ISIC archive website: ISIC Archive Kaggle Dataset Link: Search for "ISIC 2017 Skin Lesion Dataset" on Kaggle.

Although melanoma is notorious for its high degree of heterogeneity and plasticity1,2, the origin and magnitude of cell state diversity remains poorly understood. Equally, it is not known whether melanoma growth and metastatic dissemination are supported by overlapping or distinct melanoma subpopulations. By combining mouse genetics, unbiased lineage tracing and quantitative modelling, single-cell and spatial transcriptomics, we provide evidence of a hierarchical model of tumour growth that mirrors the cellular and molecular logic underlying embryonic neural crest cell fate specification and differentiation. Our findings indicate that tumorigenic competence is associated with a spatially localized perivascular niche environment, a phenotype acquired through a NOTCH3-dependent intercellular communication pathway established by endothelial cells. Consistent with a model in which only a fraction of melanoma cells is fated to fuel growth, temporal single-cell tracing of a population of melanoma cells harbouring a mesenchymal-like state revealed that these cells do not contribute to primary tumour growth but, instead, constitutes a pool of metastatic-initiating cells that can switch cell identity while disseminating to secondary organs. Our data provide a spatially and temporally resolved map of the diversity and trajectories of cancer cell states within the evolving melanoma ecosystem and suggest that the ability to support growth and metastasis are limited to distinct pools of melanoma cells. The observation that these phenotypic competencies can be dynamically acquired upon exposure to specific niche signals warrant the development of therapeutic strategies that interfere with the cancer cell reprogramming activity of such microenvironmental cues.

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?

Supplemental methods and data for JAAD International Publication

In 2022, Australia had the fourth-highest total number of skin cancer cases worldwide and the highest age-standardized rate, with roughly 37 cases of skin cancer per 100,000 population. The graph illustrates the rate of skin cancer in the countries with the highest skin cancer rates worldwide in 2022.

The global non-melanoma skin cancer (NMSC) drugs market is projected for substantial growth, driven by an increasing prevalence of NMSC globally, a growing aging population, and advancements in treatment modalities. Estimated at approximately $5,500 million in 2025, the market is expected to expand at a compound annual growth rate (CAGR) of around 7.5% through 2033. This robust expansion is primarily fueled by rising awareness campaigns, early diagnostic capabilities, and the introduction of novel therapeutic options, including targeted therapies and immunotherapies, which offer improved efficacy and patient outcomes. The increasing burden of skin cancer, particularly among individuals with fair skin and those exposed to prolonged UV radiation, further propels the demand for effective NMSC treatments. The market dynamics are further shaped by a continuous stream of research and development activities aimed at creating more personalized and less toxic treatment regimens. Key drivers include the rising incidence of basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), the two most common forms of NMSC. While the market is poised for significant expansion, certain restraints such as the high cost of advanced therapies and the availability of less expensive, albeit less effective, treatment alternatives could pose challenges. However, the growing healthcare expenditure, favorable reimbursement policies in developed nations, and the expanding market access for innovative drugs are expected to mitigate these restraints. Geographically, North America and Europe are anticipated to dominate the market, owing to advanced healthcare infrastructure and high NMSC diagnosis rates, with Asia Pacific emerging as a rapidly growing region due to increasing healthcare investments and a rising patient pool. This report delves into the dynamic non-melanoma skin cancer (NMSC) drugs market, providing an in-depth analysis of its current state and future trajectory. Spanning the study period of 2019-2033, with a base and estimated year of 2025, this research offers critical insights for stakeholders navigating this evolving landscape. The report meticulously examines the market's concentration, key trends, regional dominance, product advancements, driving forces, challenges, and emerging opportunities. With a focus on the United States and global markets, and a detailed segmentation by application, types, and industry developments, this report is an indispensable tool for strategic decision-making.

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 dataset, released September 2020, contains data on the female cancer incidences during 2010-2014 by Breast cancer, Colorectal Cancer, Melanoma of the skin, Lung cancer, Uterine cancer, Lymphoma cancer, Leukaemia cancer, Thyroid cancer, Ovarian cancer, Pancreatic cancer, All other cancers and All cancers combined. The data is by Local Government Area (LGA) 2016 geographic boundaries.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU from an analysis by the Australian Institute of Health and Welfare (AIHW) of the Australian Cancer Database (ACD) 2015. The ACD is compiled at the AIHW from cancer data provided by state and territory cancer registries: for further information on the ACD see link. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

Description

Welcome! This dataset, comprising 13,900 meticulously curated images, is a valuable resource for advancing the field of dermatology and computer-aided diagnostics. Dive into the intricate world of melanoma, where every pixel holds the potential to redefine early detection.

Context

Melanoma, a deadly form of skin cancer, demands prompt and accurate diagnosis. Leveraging state-of-the-art technology, this dataset empowers researchers and practitioners to develop robust machine-learning models capable of distinguishing between benign and malignant lesions. The images, uniformly sized at 224 x 224 pixels, offer a comprehensive view of melanoma's diverse manifestations.

Sources and Inspiration

This dataset draws inspiration from the critical need for advanced diagnostic tools in dermatology. The images are compiled from diverse sources and showcase the intricate features that challenge traditional diagnostic methods. By sharing this dataset on Kaggle, we invite the global data science community to collaborate, innovate, and contribute towards developing reliable models for melanoma classification.

How to Participate

1. Download the dataset and explore the wide variety of melanoma images.
2. Develop machine learning models for accurate classification.
3. Share your insights, challenges, and breakthroughs with the Kaggle community.
4. Together, let's push the boundaries of melanoma diagnosis and make a lasting impact on healthcare.

Join me in this exciting endeavor to enhance early detection, improve patient outcomes, and make strides in the fight against melanoma. The future of dermatology is in your hands!

This dataset, released September 2017, contains data on the total cancer incidences during 2006-2010 by Colorectal Cancer, Melanoma of the skin, Lung cancer, Lymphoma cancer, Leukaemia cancer, …Show full descriptionThis dataset, released September 2017, contains data on the total cancer incidences during 2006-2010 by Colorectal Cancer, Melanoma of the skin, Lung cancer, Lymphoma cancer, Leukaemia cancer, Pancreatic cancer, and All cancers combined. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Compiled by PHIDU from an analysis by the Australian Institute of Health and Welfare (AIHW) of the Australian Cancer Database (ACD) 2012. The ACD is compiled at the AIHW from cancer data provided by state and territory cancer registries. Please note: AURIN has spatially enabled the original data. "*" - Indicates statistically significant, at the 95% confidence level. "**" - Indicates statistically significant, at the 99% confidence level. "~" - Indicates modelled estimates have Relative Root Mean Square Errors (RRMSEs) from 0.25 to 0.50 and should be used with caution. "~~" - Indicates modelled estimates have RRMSEs greater than 0.50 but less than 1 and are considered too unreliable for general use. '?' - Indicates modelled estimates are considered too unreliable. Blank cell - Indicates data was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data). Abbreviation Information: "ASR per #" - Indirectly age-standardised rate per specified population. "SR" - Indirectly age-standardised ratio. "95% C.I" - upper and lower 95% confidence intervals. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

This dataset, released September 2017, contains data on the male cancer incidences during 2006-2010 by Prostate cancer, Colorectal Cancer, Melanoma of the skin, Lung cancer, Head and neck cancer, …Show full descriptionThis dataset, released September 2017, contains data on the male cancer incidences during 2006-2010 by Prostate cancer, Colorectal Cancer, Melanoma of the skin, Lung cancer, Head and neck cancer, Lymphoma cancer, Leukaemia cancer, Bladder cancer, Kidney cancer, Pancreatic cancer, Stomach cancer, All other cancers and All cancers combined. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Compiled by PHIDU from an analysis by the Australian Institute of Health and Welfare (AIHW) of the Australian Cancer Database (ACD) 2012. The ACD is compiled at the AIHW from cancer data provided by state and territory cancer registries. Please note: AURIN has spatially enabled the original data. "*" - Indicates statistically significant, at the 95% confidence level. "**" - Indicates statistically significant, at the 99% confidence level. "~" - Indicates modelled estimates have Relative Root Mean Square Errors (RRMSEs) from 0.25 to 0.50 and should be used with caution. "~~" - Indicates modelled estimates have RRMSEs greater than 0.50 but less than 1 and are considered too unreliable for general use. '?' - Indicates modelled estimates are considered too unreliable. Blank cell - Indicates data was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data). Abbreviation Information: "ASR per #" - Indirectly age-standardised rate per specified population. "SR" - Indirectly age-standardised ratio. "95% C.I" - upper and lower 95% confidence intervals. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)