The Future of Precision Prevention for Advanced Melanoma Article Swipe
Katie J. Lee
,
Brigid Betz‐Stablein
,
Mitchell Stark
,
Monika Janda
,
Aideen McInerney‐Leo
,
Liam J Caffery
,
Nicole Gillespie
,
Tatiane Yanes
,
H. Peter Soyer
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fmed.2021.818096
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fmed.2021.818096
Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular total body photography with sequential digital dermoscopic imaging. This approach aims to address both underdiagnosis (a missed or delayed melanoma diagnosis) and overdiagnosis (the diagnosis and treatment of indolent lesions that would not have caused a problem). Holistic risk stratification considers several types of melanoma risk factors: clinical phenotype, comprehensive imaging-based phenotype, familial and polygenic risks. Artificial intelligence computer-aided diagnostics combines these risk factors to produce a personalized risk score, and can also assist in assessing the digital and molecular markers of individual lesions. However, to ensure uptake and efficient use of AI systems, researchers will need to carefully consider how best to incorporate privacy and standardization requirements, and above all address consumer trust concerns.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmed.2021.818096
- https://www.frontiersin.org/articles/10.3389/fmed.2021.818096/pdf
- OA Status
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- Cited By
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- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4205539841Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fmed.2021.818096Digital Object Identifier
- Title
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The Future of Precision Prevention for Advanced MelanomaWork title
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articleOpenAlex work type
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-17Full publication date if available
- Authors
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Katie J. Lee, Brigid Betz‐Stablein, Mitchell Stark, Monika Janda, Aideen McInerney‐Leo, Liam J Caffery, Nicole Gillespie, Tatiane Yanes, H. Peter SoyerList of authors in order
- Landing page
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https://doi.org/10.3389/fmed.2021.818096Publisher landing page
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https://www.frontiersin.org/articles/10.3389/fmed.2021.818096/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/fmed.2021.818096/pdfDirect OA link when available
- Concepts
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Overdiagnosis, Risk stratification, Standardization, Precision medicine, Risk assessment, Computer science, Melanoma diagnosis, Medicine, Medical physics, Melanoma, Risk analysis (engineering), Pathology, Internal medicine, Computer security, Cancer research, Operating systemTop concepts (fields/topics) attached by OpenAlex
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15Total citation count in OpenAlex
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2025: 5, 2024: 4, 2023: 1, 2022: 5Per-year citation counts (last 5 years)
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74Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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