Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer Article Swipe
Kate Murray
,
Lucy Oldfield
,
Irena Stefanova
,
Manuel Gentiluomo
,
Paolo Aretini
,
Rachel O’Sullivan
,
William Greenhalf
,
Salvatore Paiella
,
Mateus Nóbrega Aoki
,
Aldo Pastore
,
James Birch-Ford
,
Bhavana Hemantha Rao
,
Pinar Uysal‐Onganer
,
Caoimhe Walsh
,
George B. Hanna
,
Jagriti Narang
,
Pradakshina Sharma
,
Daniele Campa
,
Cosmeri Rizzato
,
Andrei Turtoï
,
Elif Sever
,
Alessio Felici
,
Ceren Sucularlı
,
Giulia Peduzzi
,
Efkan Öz
,
Uğur Sezerman
,
Robert Van Der Meer
,
Nathan E. Thompson
,
Eithne Costello
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.semcancer.2025.02.009
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.semcancer.2025.02.009
Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.
Related Topics
Concepts
Biomarker
Pancreatic ductal adenocarcinoma
Pancreatic cancer
Omics
Biomarker discovery
Medicine
Disease
Population
Cancer
Bioinformatics
Internal medicine
Biology
Proteomics
Environmental health
Gene
Biochemistry
Metadata
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- Language
- en
- Landing Page
- https://doi.org/10.1016/j.semcancer.2025.02.009
- OA Status
- hybrid
- Cited By
- 15
- References
- 183
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407764510
All OpenAlex metadata
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https://openalex.org/W4407764510Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.semcancer.2025.02.009Digital Object Identifier
- Title
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Biomarkers, omics and artificial intelligence for early detection of pancreatic cancerWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-20Full publication date if available
- Authors
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Kate Murray, Lucy Oldfield, Irena Stefanova, Manuel Gentiluomo, Paolo Aretini, Rachel O’Sullivan, William Greenhalf, Salvatore Paiella, Mateus Nóbrega Aoki, Aldo Pastore, James Birch-Ford, Bhavana Hemantha Rao, Pinar Uysal‐Onganer, Caoimhe Walsh, George B. Hanna, Jagriti Narang, Pradakshina Sharma, Daniele Campa, Cosmeri Rizzato, Andrei Turtoï, Elif Sever, Alessio Felici, Ceren Sucularlı, Giulia Peduzzi, Efkan Öz, Uğur Sezerman, Robert Van Der Meer, Nathan E. Thompson, Eithne CostelloList of authors in order
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https://doi.org/10.1016/j.semcancer.2025.02.009Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.semcancer.2025.02.009Direct OA link when available
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Biomarker, Pancreatic ductal adenocarcinoma, Pancreatic cancer, Omics, Biomarker discovery, Medicine, Disease, Population, Cancer, Bioinformatics, Internal medicine, Biology, Proteomics, Environmental health, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
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2025: 15Per-year citation counts (last 5 years)
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| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 7 |
| institutions_distinct_count | 29 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.99895356 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |