Evidence map›Paper›PMID 42193319›Full record

ReviewBiomedicines2026

Factors Associated with Stage at Diagnosis in Pancreatic Cancer: Implications for Precision Screening and Early Detection.

Elen Deng, Manvita Mareboina, Ilias Georgakopoulos-Soares, Nelson S Yee

Abstract readReview
In one paragraph

Review in Biomedicines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Elen DengDepartment of Molecular and Precision Medicine, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, PA 17033, USA.ORCID 0000-0001-5334-8455
Manvita MareboinaDepartment of Molecular and Precision Medicine, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, PA 17033, USA.
Ilias Georgakopoulos-SoaresDepartment of Molecular and Precision Medicine, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, PA 17033, USA.ORCID 0000-0003-3641-1488
Nelson S YeeDepartment of Medicine, Division of Hematology-Oncology, Penn State Health Milton S. Hershey Medical Center, Hershey, PA 17033, USA.ORCID 0000-0002-1457-9047

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pancreatic cancer is a leading cause of cancer-related mortality in the United States and worldwide. Most patients are diagnosed with pancreatic cancer at advanced stages, when curative therapy is no longer possible. The stage of pancreatic cancer at diagnosis critically impacts the treatment options and thus the clinical outcomes. Currently, there is no established screening program or tests for its early detection. Studying and understanding how those factors influence the stage of pancreatic cancer at diagnosis helps identify barriers and develop screening strategies. Tumoral and demographic factors, as well as social determinants of health, tend to be associated with localized vs. advanced stage of pancreatic cancer at diagnosis. Socioeconomic factors have been shown to be important mediators of racial disparities in stage at diagnosis as well as germline genetic testing. Recently, screening initiatives, blood-based molecular biomarker tests for early detection of pancreatic cancer, and machine learning-based models for risk prediction and imaging diagnostics have been developed. By determining and understanding the factors associated with the stage at diagnosis, risk-stratified screening can be feasible by combining demographics, genetics, comorbidities, lifestyle, and social determinants. Moreover, regulatory policies that address the social determinants of health can guide the development of screening strategies to allocate resources for equitable access to healthcare and to reduce disparities in patients with pancreatic cancer.

Indexed as

biomarkersdisparities early detectionelectronic health recordsextracellular vesiclesmachine learningpancreatic cancerscreeningsocial determinants of healthstage at diagnosis

Identifiers

PMID42193319
PMCPMC13204872

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.