This dataset contains raw RNA sequencing data from melanoma patient tumor samples collected before and during immunotherapy. The data support investigation of treatment-related changes in tumor cell populations. Samples were analyzed as part of a study exploring cellular responses to immune-based therapies.

Genome and transcriptome sequence data from a melanoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

This population-based cohort study performed utilizing the TriNetX database compares the risk of subsequent primary malignancies in patients diagnosed with melanoma or non-melanoma skin cancer, including basal cell carcinomas and squamous cell carcinomas. A case cohort was identified using ICD-10-CM codes for melanoma, basal cell carcinoma, and squamous cell carcinoma, as well as 11 of the most common primary malignancies as defined by the American Cancer Society. These included breast, prostate, lung, colorectal, bladder, non-Hodgkin’s lymphoma, kidney, uterine, leukemia, pancreas, and thyroid cancer.

A total of 130,526 melanoma patients and 130,526 non-melanoma skin cancer patients were identified after propensity score matching for demographics. The adjusted risk ratios revealed that melanoma patients had a significantly increased risk of developing breast, prostate, lung, kidney, pancreatic, and thyroid cancer compared to NMSC patients. No significant differences were found in the risk of developing colorectal cancer, prostate cancer, bladder cancer, non-Hodgkin’s lymphoma, or leukemia between the two groups.

Overall, this TriNetX database study underscores the importance of tailored cancer surveillance, particularly for breast, prostate, lung, kidney, pancreatic, and thyroid cancer in melanoma survivors. Future research should focus on exploring the mechanisms behind the differential cancer risks observed in this study and refining screening strategies for higher-risk populations.

Promoting standardization and quality assurance (QA) in oncology on the strength of real-world data is essential to ensure better patient outcomes. Wide excision after primary tumor biopsy is a fundamental step in the therapeutic pathway for cutaneous malignant melanoma (CMM). The aim of this population-based cohort study is to assess adherence to wide local excision in a cohort of patients diagnosed with CMM and the impact of this recommended procedure on overall and disease-specific survival.This retrospective cohort study concerns CMM patients diagnosed in the Veneto region (north-east Italy) in 2017, included in the high-resolution Veneto Cancer Registry, and followed up through linkage with the regional mortality registry up until February 29th, 2020. Using population-level real-world data, linking patient-level cancer registry data with administrative records of clinical procedures may shed light on the real-world treatment of CMM patients in accordance with current guidelines. After excluding TNM stage IV patients, a Cox regression analysis was performed to test whether the completion of a wide local excision was associated with a difference in melanoma-specific and overall survival, after adjusting for other covariates. No wide excision after the initial biopsy was performed in 9.7% of cases in our cohort of 1,305 patients. After adjusting for other clinical prognostic characteristics, Cox regression revealed that failure to perform a wide local excision raised the hazard ratio of death in terms of overall survival (HR = 4.80, 95% CI: 2.05-11.22, p < 0.001) and melanoma-specific survival (HR = 2.84, 95% CI: 1.04-7.76, p = 0.042). By combining clinical and administrative data, this study on real-world clinical practice showed that almost one in ten CMM patients did not undergo wide local excision surgery. Monitoring how diagnostic-therapeutic protocols are actually implemented in the real world may contribute significantly to promoting quality improvements in the management of oncological patients.

In patients with metastatic melanoma, the identification and validation of accurate prognostic biomarkers will assist rational treatment planning. Studies based on "-omics" technologies have focussed on a single high-throughput data type such as gene or microRNA transcripts. Occasionally, these features were evaluated in conjunction with limited clinico-pathologic data. With the increased availability of multiple data types, there is a pressing need to tease apart which of these sources contain the most valuable prognostic information. We evaluated and integrated several data types derived from the same tumor specimens in AJCC stage III melanoma patients - gene, protein, and microRNA expression as well as clinical, pathologic and mutation information - to determine their relative impact on prognosis. We used classification frameworks based on pre-validation and bootstrap multiple imputation classification to compare the prognostic power of each data source, both individually as well as integratively. We found that the prognostic utility of clinico-pathologic information was not out-performed by various "-omics" platforms. Rather, a combination of clinico-pathologic variables and mRNA expression data performed best. Furthermore, a patient-based classification analysis revealed that the prognostic accuracy of various data types was not the same for different patients, providing useful insights for ongoing developments in the individualized treatment of melanomas patients. SPECIAL NOTE: In this study, survival data were re-extracted from the MIA research database for all patients and brought up to date, revealing discrepancies affecting survival class in the case of four patients compared with the previous dataset (GSE53118: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53118). The current survival data are considered to be more accurate although the expression information has not changed. In addition, there were 5 samples (122, 144, 195, 264, and 358) for which gene expression information were not available at the time of analysis. However, the associated clinical information for these samples is provided since it was analysed elsewhere in the accompanying publication. Samples eligible for this study (n=84) were obtained from lymph node specimens (Melanoma Institute Australia (MIA) Biospecimen Bank) in which macroscopic tumor was observed, obtained from patients believed to be without distant metastases at the time of tumor banking based on clinical examination and computerised axial tomographic scanning of the brain, chest, abdomen and pelvis. Specimens were macro-dissected at time of banking and subsequently reviewed to meet minimum criteria for tumor cell content (>80%) and amount of necrosis (<30%). Linked clinical and pathologic data were obtained from the MIA research database. We previously analyzed the distribution of survival times in these samples and identified more favorable and less favorable groups as patients having time from surgery to death from melanoma greater than 4 years with no sign of relapse (n=25) or less than 1 year (n=22), respectively (Mann et al. 2013, PMID: 22931913). Since that publication, survival data have been re-extracted from the MIA research database for all patients and brought up to date, revealing discrepancies affecting survival class in the case of four patients compared with the previous dataset. The current survival data are considered to be more accurate. MRNA expression profiling and somatic mutation profiling, were performed as previously described in Mann et al. 2013 (PMID: 22931913).

Single cell RNA libraries were prepared using 10x Genomics Chromium Next GEM Single Cell 3’ v2 reagents. The samples were barcoded and each library were pooled with two samples at equimolar concentrations. The pooled libraries (n=4) were sequenced on the NextSeq 500 machine (Illumina) with paired-end sequencing and dual indexing as recommended in the manufacturer’s protocol; 26 and 98 cycles for the respective Read 1 and 2, and 8 cycles for i7 index.

We introduce the Melanoma Research Alliance Multimodal Image Dataset for AI-based Skin Cancer (MRA-MIDAS) dataset, the first publicly available, prospectively-recruited, systematically-paired dermoscopic and clinical image-based dataset across a range of skin-lesion diagnoses. This dataset encompasses a wide array of skin lesions and includes well-annotated, patient-level, clinical metadata. It aims to more accurately mirror real-world clinical scenarios than retrospectively curated datasets and is enhanced by extensive histopathologic confirmation to ensure data integrity. This research was approved by the Institutional Review Board at Stanford University under IRB#36050, along with the Cleveland Clinic Foundation under IRB#20-666, and adhered to the Helsinki Declaration. Patients presenting to the dermatology clinics of participating dermatologists at Stanford Medicine or Cleveland Clinic Foundation between August 18, 2020, and April 17, 2023, were eligible for the study if 1) they had at least one solitary skin lesion of concern identified where a skin biopsy was deemed medically necessary by the dermatologist investigator or 2) patients were directed to in-clinic evaluation for a lesion that was previously identified as concerning through a teledermatology encounter or dermatologist review of a patient photo submitted through the electronic patient messaging portal. Patients underwent written informed consent with either the physician or research coordinator, after which both clinical and dermoscopic digital photography were obtained of any eligible skin lesions. Each lesion underwent standardized photography with a contemporary model iPhone or iPad device (iPhone SE to iPhone 12 Pro and iPod touch to iPad mini) without flash photography at 15-cm and 30-cm distances, along with digital dermatoscope photography. For each lesion, clinical information about the patient was obtained and recorded including sex assigned at birth, age, Fitzpatrick skin type, personal history of melanoma, anatomic location, and the lesion’s length and width. Investigators had the discretion to identify additional control lesions that clinically appeared benign on a corresponding contralateral body site that were similarly enrolled for digital photography as an un-biopsied control lesion to include in the dataset, though model analysis was restricted to biopsied lesions. This dataset contains images obtained from patients at Stanford who provided consent for public release of their images and represents the near entirety of cases enrolled at this site. At the time of first enrollment, the Stanford dermatologists at the specialized pigmented lesion and melanoma clinics had an average of 15.7 years of post-residency experience while those in general medical dermatology clinics had an average of 3.9 years’ experience. Dermatologists noted their top-five ranked clinical impressions at the time of evaluation, along with their binary level of confidence (Yes/No) in their top impression. For any biopsied lesions, associated histopathologic final diagnoses were recorded and categorized into a previously described taxonomy. Biopsy results were interpreted by three board-certified dermatopathologists at Stanford. A dermatopathology consensus conference reviewed any diagnosis of severely dysplastic melanocytic nevus or worse. Melanocytic lesions were specifically grouped in the following manner: benign melanocytic nevi, melanomas (including melanoma in-situ and invasive melanoma), and surgically-eligible intermediate melanocytic tumors where complete excision is typically recommended (including severely dysplastic melanocytic nevi and melanocytomas such as typical/atypical Spitz tumors, such as BAP-1-inactivated melanocytic tumors, deep penetrating nevi/tumors, and cellular blue nevi with atypia). Cases were included in the dataset if a second reviewing independent board-certified dermatologist agreed with the favored diagnosis based on a review of the associated images. Funding: This project is based on research supported by the Melanoma Research Alliance (MRA)- L’Oreal Dermatological Beauty Brands Team Science Award, along with philanthropic funding from the David Mair and Vanessa Vu-Mair Artificial Intelligence in Skin Cancer Fund and the Tal & Cinthia Simon Melanoma Research Fund at Stanford Medicine. Acknowledgments: This material is the result of work supported with resources and the use of facilities at the Veterans Affairs Palo Alto Health Care System in Palo Alto, California.

Genome and transcriptome sequence data from a metastatic uveal melanoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

Patient-derived xenografts (n=96) were derived from metastatic melanoma patients. RNA expression profiling will be preformed to study 1. HLA-typing and 2. the effect of the tumour microenvironment on tumour growth

This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

Patient and disease characteristics of first primary melanoma (N = 7,654).

Established surgical and pathology melanoma quality indicators and feasibility of collection with administrative data.

Melanoma Detection Market Outlook

The melanoma detection market size was valued at approximately USD 3.2 billion in 2023 and is projected to reach USD 6.1 billion by 2032, growing at a CAGR of 7.4% during the forecast period. This growth is primarily driven by the increasing incidence of skin cancer globally, coupled with advancements in diagnostic technologies that facilitate early detection and improve patient outcomes. The rising awareness about skin health and the importance of early cancer detection are significant driving forces, encouraging the adoption of advanced diagnostic tools across various healthcare settings.

One of the key growth factors in this market is the technological advancements in diagnostic methods. The integration of artificial intelligence (AI) in melanoma detection is revolutionizing the way dermatologists and oncologists identify and diagnose skin cancers. AI-based systems, with their ability to analyze vast amounts of data and recognize patterns, offer higher accuracy and efficiency compared to traditional methods. This not only aids in the early detection and treatment of melanoma but also reduces the burden on healthcare professionals, allowing for more streamlined and effective patient care. Furthermore, the development of non-invasive diagnostic methods, such as imaging techniques and dermatoscopy, enhances the patient experience by minimizing discomfort and the need for surgical procedures.

Another significant factor contributing to market growth is the increasing prevalence of melanoma, driven by factors such as prolonged exposure to ultraviolet (UV) radiation, a major risk factor for skin cancer. The growing awareness about the harmful effects of UV radiation and the importance of protective measures, such as sunscreen use and regular skin check-ups, is encouraging individuals to seek diagnostic services. Additionally, the aging population, which is more susceptible to skin cancers, is driving demand for advanced melanoma detection methods. As healthcare systems worldwide focus on prevention and early detection, the demand for effective melanoma diagnostic tools is anticipated to rise steadily.

Moreover, government initiatives and funding for cancer research are significantly propelling the melanoma detection market. Public health campaigns aimed at raising awareness about skin cancer and the benefits of early detection are increasing the adoption of advanced diagnostic techniques. Governments and non-profit organizations are investing heavily in research and development to innovate and improve existing diagnostic technologies, thereby boosting the market. This, combined with favorable reimbursement policies for melanoma diagnostic procedures in several countries, is anticipated to further fuel market growth over the forecast period.

Regionally, North America holds the largest share in the melanoma detection market, driven by a high incidence rate of skin cancer, advanced healthcare infrastructure, and strong presence of key market players. The market in Europe is also growing significantly, supported by robust healthcare systems and increasing investments in cancer research. The Asia Pacific region is expected to witness the fastest growth, attributed to rising healthcare expenditure, increasing awareness about skin cancer, and improving healthcare facilities. Latin America and the Middle East & Africa, although currently smaller markets, are anticipated to grow steadily due to rising awareness and improving access to advanced diagnostic technologies.

In the realm of melanoma detection, the role of Melanocyte Stimulating Hormone Receptor (MSHR) is gaining attention for its potential implications in skin cancer research. MSHR is a critical component of the melanocortin system, which regulates pigmentation and plays a role in the body's response to UV radiation. Understanding the function and expression of MSHR in melanocytes can provide valuable insights into the mechanisms underlying melanoma development. Recent studies suggest that variations in the MSHR gene may influence susceptibility to melanoma, making it a focal point for genetic research and potential therapeutic interventions. As the scientific community continues to explore the genetic factors contributing to melanoma, MSHR remains a promising target for future studies aimed at improving diagnostic accuracy and developing personalized treatment strategies.

Diagnostic Method Analysis

The melanoma detection market is segmen

This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.

Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name "CPTAC-cancertype". Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available. For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. Pathology imaging is collected as part of the CPTAC qualification workflow.

All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable. There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term "discovery proteomics" is in reference to "untargeted" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term “targeted proteomics” refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein in vivo. Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the CPTAC Imaging Proteomics page.

CPTAC Imaging Special Interest Group

You can join the CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses! Artifacts from previous webinars such as slide decks and video recordings can be found on the CPTAC SIG Webinars page.

Introduction: Phase III studies of PD-1 inhibitors have demonstrated remarkable improvements in the survival of patients with metastatic melanoma (MM). If these results are generalizable to an unselected patient population treated in clinical routine is unknown. This study aimed to investigate and describe clinical efficacy and safety of PD-1 inhibitors in patients with MM treated in routine clinical practice. Material and methods: A retrospective descriptive study of patients with metastatic or inoperable cutaneous melanoma treated with PD-1 inhibitors at a single institution (Department of Oncology, Sahlgrenska University Hospital) from 1 September 2015 to 31 August 2017. Data were obtained from medical records. Results: A total of 116 patients were included in the analyses. The overall survival (OS) at 12-month follow-up was 70.2% and the median OS was 27.9 months. Patients with BRAF mutated tumors had increased OS, whereas ECOG PS ≥2, LDH > ULN and presence or history of brain metastases (stage M1d) were associated with impaired survival. Immune-related AEs of any grade occurred in 64 (55.2%) patients and 15 (12.9%) patients experienced immune-related AEs of grades 3 and 4. Notably, rheumatic adverse events occurred at a higher rate (15.5%) than previously reported. The occurrence of immune-related AEs was associated with a benefit in OS, while the severity of immune-related AEs did not affect survival, nor did the use of systemic corticosteroids. Conclusions: The efficacy and safety of PD1 inhibitors in routine clinical practice appear comparable to that described in clinical trials.

MAPK inhibitors have significantly improved overall survival in patients with metastatic melanoma disease but their efficacy is still limited by primary or acquired resistance. Several studies have attempted to predict response to MAPK inhibitor therapy, however the lack of a consistent cohort prevents better definition of associations between treatment efficacy and clinical and/or molecular features. Here, we present MelanoDB, a collection of patients with metastatic melanoma treated with MAPK inhibitors. We formatted data from 8 different studies for a total of 417 cases to gather common clinical and molecular features. Whole or partial exome sequencing is available for 191 cases and gene expression for 132 cases. We provide a web application to explore the integrated data and its distribution among the collected studies, and we share this dataset to the scientific community according to FAIR principles

Here we provide a web application viewer of the database content: http://melanodb-ircm.montp.inserm.fr/

You are requested to cite this repository in the case of using these data in a publication.

Digital Melanoma Risk Calculator Market Outlook

According to our latest research, the global digital melanoma risk calculator market size reached USD 312.5 million in 2024, with a robust CAGR of 14.2% expected between 2025 and 2033. This dynamic growth trajectory is set to propel the market to a forecasted value of USD 1,018.7 million by 2033. The primary growth driver is the escalating incidence of melanoma and other skin cancers globally, which has heightened the demand for early, accurate, and accessible diagnostic tools such as digital melanoma risk calculators. As per the latest research, the market is witnessing significant investments in artificial intelligence (AI) and machine learning (ML) integration, which are further enhancing diagnostic accuracy and expanding the adoption of these solutions across diverse healthcare environments.

One of the most significant growth factors for the digital melanoma risk calculator market is the increasing global prevalence of melanoma, especially in regions with high ultraviolet (UV) radiation exposure. Rising public awareness about the importance of early skin cancer detection has driven both patients and healthcare providers to adopt advanced digital solutions. These calculators leverage sophisticated algorithms to assess risk based on patient data, clinical images, and genetic information, enabling clinicians to make more informed decisions. Additionally, the proliferation of smartphones and wearable devices has made it easier for individuals to monitor their skin health, thereby integrating digital melanoma risk calculators into daily healthcare routines. This convergence of technology and healthcare is not only improving patient outcomes but also reducing the burden on healthcare systems by facilitating early intervention and reducing the need for invasive diagnostic procedures.

Another vital driver fueling the marketÂ’s expansion is the rapid technological advancements in AI and deep learning. The integration of these technologies into digital melanoma risk calculators has dramatically increased their predictive accuracy and reliability. Modern solutions can now analyze large datasets, including dermoscopic images and patient histories, to provide precise risk assessments within seconds. Furthermore, the growing adoption of cloud-based healthcare solutions has enabled seamless data sharing and collaboration among healthcare professionals, enhancing the overall efficiency of melanoma diagnosis and risk management. These technological innovations are also encouraging the development of user-friendly interfaces, making these tools accessible to a broader audience, including primary care physicians and even patients themselves. The result is a democratization of melanoma risk assessment, empowering more individuals to take proactive steps toward their skin health.

Supportive government policies and initiatives aimed at reducing cancer mortality rates are also playing a crucial role in driving the digital melanoma risk calculator market. Many countries have launched national skin cancer screening programs and are investing in digital health infrastructure to improve early detection rates. In parallel, collaborations between public health agencies, research institutions, and technology providers are fostering innovation and accelerating the deployment of advanced risk assessment tools. Reimbursement policies for digital diagnostic solutions are gradually being introduced, further incentivizing healthcare providers to integrate these calculators into their clinical workflows. These collective efforts are creating a conducive environment for market growth, ensuring that digital melanoma risk calculators become an integral part of comprehensive cancer care strategies worldwide.

In recent years, the emergence of Skin Cancer Screening Apps has revolutionized the landscape of dermatological healthcare. These apps utilize advanced algorithms and AI technology to analyze images of skin lesions, providing users with an initial assessment of potential skin cancer risks. By offering a convenient and accessible way for individuals to monitor their skin health, these apps are playing a crucial role in early detection and prevention strategies. As smartphone penetration continues to rise globally, the adoption of these apps is expected to increase, particularly in regions with limited access to dermatology specialists. The integration o

The Cancer Moonshot Biobank is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who are receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites.

This collection contains de-identified radiology and histopathology imaging procured from subjects in NCI’s Cancer Moonshot Biobank-Melanoma (CMB-MEL) cohort. Associated genomic, phenotypic and clinical data will be hosted by The Database of Genotypes and Phenotypes (dbGaP) and other NCI databases. A summary of Cancer Moonshot Biobank imaging efforts can be found on the Cancer Moonshot Biobank Imaging page.

In the advanced stages, malignant melanoma (MM) has a very poor prognosis. Due to tremendous efforts in cancer research, the overall survival of metastatic melanoma has doubled; however, complete eradication of the disease is almost unknown. With the advent of proteomics, deep-mining studies can reach low-abundant expression areas. The complexity of the proteome, however, still surpasses the dynamic range capabilities of current analytical techniques. Consequently, many predicted protein products with potential biological functions have not yet been verified in experimental proteomic data. This category of ‘missing proteins’ (MP) is comprised of all proteins that have been predicted but are currently unverified. As part of the initiative launched in 2016 in the United States, the European Cancer Moonshot Center has performed numerous deep proteomics analyses on samples from MM patients. In this study, nine MPs were clearly identified by mass spectrometry in MM metastases. Some MPs significantly-correlated with proteins that possess identical PFAM structural domains; and other MPs were significantly-associated with cancer-related proteins. This is the first study to our knowledge, where unknown and novel proteins have been annotated within metastasis, from Melanoma Cancer patients.

From the dbGaP study description: "In this study, we performed paired tumor/normal long insert whole genome and exome sequencing and tumor RNA sequencing on primary or metastatic acral melanoma tumors collected from 34 patients. Patients were enrolled from either Vanderbilt University or the Memorial Sloan-Kettering Cancer Center. We report an integrated analysis of DNA and RNA sequencing data to describe genomic and transcriptomic characteristics of acral melanoma. The data includes information about the study, subject phenotype datasets (de-identified subject IDs, disease onset age, subject gender, and subject race), and molecular datasets (SRA run information)."

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Non-Melanoma Skin Cancer Market Size 2024-2028

The non-melanoma skin cancer market size is forecast to increase by USD 136.9 million at a CAGR of 4.5% between 2023 and 2028.

The non-melanoma skin cancer (NMSC) market is experiencing significant growth due to the increasing incidence of this type of cancer. NMSC is the most common form of cancer, with over three million cases diagnosed annually worldwide. The market is facing a challenge due to the lack of drugs in the pipeline for NMSC treatment. This trend is expected to continue, as current treatments such as surgical excision and Mohs micrographic surgery have limitations and may leave scars or require multiple procedures. Healthcare services are playing a critical role in addressing this issue by improving early detection, offering advanced treatment options, and providing patient support throughout the recovery process. Additionally, the aging population and rising awareness about cosmetic skin care are contributing to the market's growth. The market analysis report provides an in-depth analysis of these trends and growth factors, offering valuable insights for stakeholders In the healthcare industry.

What will be the Size of the Non-Melanoma Skin Cancer Market During the Forecast Period?

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The market encompasses a range of conditions, including basal cell carcinoma and squamous cell carcinoma. These forms of cancer are primarily caused by sun exposure, resulting in symptoms such as scaly surfaces, red patches, sores, moles, or warts. Treatment modalities include surgery, radiation therapy, photodynamic therapy, topical therapies, cryosurgery, and electrodesiccation. Mohs surgery, a specialized form of surgery, is increasingly utilized for high-risk occurrences due to its ability to preserve healthy tissue.
Advanced treatments, such as Vismodegib, offer promising alternatives for patients with recurrent or metastatic disease. The market is driven by the increasing prevalence of sun exposure-related injuries and the growing aging population. Despite advancements in treatment options, the risk of recurrence remains a significant concern, necessitating ongoing research and innovation.

How is this Non-Melanoma Skin Cancer Industry segmented and which is the largest segment?

The non-melanoma skin cancer industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  BCC
  SCC


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  Asia

    China
    Japan


  Rest of World (ROW)

By Type Insights

The bcc segment is estimated to witness significant growth during the forecast period.

Non-Melanoma Skin Cancer (NMSC), primarily comprised of Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC), accounts for a significant portion of cancer occurrences worldwide. BCC, responsible for approximately 80% of NMSC, has seen an annual growth rate of 2% In the US and 5% in Europe. In the Asia Pacific region, Australia holds the highest incidence of BCC among individuals aged 70. Despite a low mortality rate of approximately 0.05%, BCCs can result in disfiguring body alterations. Surgical treatments, including Mohs surgery, cryosurgery, and electrodesiccation, are common interventions. Radiation therapy, photodynamic therapy, and topical therapies also serve as alternative treatment methods.

Get a glance at the Non-Melanoma Skin Cancer Industry report of share of various segments Request Free Sample

The BCC segment was valued at USD 410.20 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 29% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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Non-Melanoma Skin Cancer (NMSC), comprised primarily of Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC), is a significant health concern In the US, accounting for approximately 35-45% of all cancers among Caucasians. NMSC is the most common cancer type In the US, with BCC being the most prevalent skin cancer subtype. Treatment modalities for NMSC include surgery, radiation therapy, photodynamic therapy, topical therapies, cryosurgery, electrodesiccation, Mohs surgery, Vismodegib for advanced cases, and chemotherapy for metastasis. Risk factors for NMSC include sun exposure, sunburns, radiation exposure, inflammation, injury, and the presence of skin growths such as bumps, moles, red patches, sores, moles, warts, and recurrence.

Market Dynamics

Our researchers analyzed the data with 2023 a