Evidence map›Paper›PMID 42787534›Full record

ArticleJournal of radiological science

Pancreatic Cyst Management: A Multimodal and Interdisciplinary Approach.

Hajra Arshad, Emir A Syailendra, Elliot K Fishman, Linda C Chu

Abstract read
In one paragraph

Article in Journal of radiological science. 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.

Hajra ArshadThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Emir A SyailendraThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Elliot K FishmanThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Linda C ChuThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Funding

Improving Management of patients at High Risk of Pancreatic CancerR01CA299421 · NCI · JOHNS HOPKINS UNIVERSITY · PI Alison P Klein, Nickolas Papadopoulos · 2025 to 2026
$1.3M
NCI NIH HHS R01 CA299421
6 · The paper itself

Abstract

With improvements in advanced cross-sectional imaging, there has been an increase in the detection of incidental pancreatic cystic lesions (PCLs). Mucinous PCLs are known precursors of pancreatic cancer, underscoring the need for early detection and appropriate management. When a PCL is initially diagnosed, the next step is to decide among three key management strategies: surgery, surveillance, or reassurance and discharge without follow-up. However, current management guidelines exhibit inconsistencies and limitations, contributing to overtreatment and increased surveillance of PCLs. This review explores the current landscape of PCL management, highlighting the roles of advanced imaging, cyst fluid biomarkers, and developments in artificial intelligence (AI) and radiomics in refining cyst classification, malignancy prediction, and clinical decision-making. Given the heterogeneity of PCLs, we advocate for a multidisciplinary approach that includes diverse clinical expertise and explainable AI model outputs to facilitate accurate risk stratification and individualized patient management.

Indexed as

Artificial Intelligencemultidisciplinary managementpancreatic cystsradiomics

Identifiers

PMID42787534
PMCPMC13602157

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.