Evidence map›Paper›PMID 39386334›Full record

ReviewMiddle East journal of digestive diseases2024

Clinical Perspectives on the Histomolecular Features of the Pancreatic Precursor Lesions: A Narrative Review.

Sayedeh-Zahra Kazemi-Harikandei, Amirali Karimi, Seyed Mohammad Tavangar

Abstract readReview
In one paragraph

Review in Middle East journal of digestive diseases, 2024. 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

3 authors.

Sayedeh-Zahra Kazemi-HarikandeiSchool of Medicine, Tehran University of Medical Sciences, Tehran, Iran.ORCID https://orcid.org/0000-0002-4985-3771
Amirali KarimiSchool of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Seyed Mohammad TavangarDepartment of Pathology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.ORCID https://orcid.org/0000-0002-4253-2385

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pancreatic cancer (PC) is a lethal cancer with poor prognoses. Identifying and characterizing pancreatic cystic lesions (PCLs) in the early detection and follow-up plans is thought to help detect pancreatic malignancy. Besides, the molecular features of PCLs are thought to unravel potentials for targeted therapies. We present a narrative review of the existing literature on the role of PCLs in the early detection, risk stratification, and medical management of PC. High-grade intraductal papillary mucinous neoplasms (IPMN) and pancreatic intraepithelial neoplasia (PanIN) stage III are high-risk lesions for developing PC. These lesions often require thorough histomolecular characterization using endoscopic ultrasound (EUS), before a surgical decision is made. EUS is also useful in the risk assessment of PCLs with tentative plans-for instance, in branch-duct IPMNs (BD-IPMN)- where the final decision might change. Besides the operative decisions, recent improvements in the application of targeted therapies are expected to improve survival measures. Knowledge of molecular features has helped develop targeted therapies. In summary, the histomolecular characterization of PCLs is helpful in optimizing management plans in PC. Further improvements are still needed for the broad application of this knowledge in the clinical setting.

Indexed as

Early detection of cancerIntraductal papillary mucinous neoplasms (IPMN)Mucinous cystic neoplasms (MCN)Pancreatic cystic lesions (PCL)Pancreatic ductal adenocarcinomaPancreatic intraepithelial neoplasms (PanIN)Targeted therapy

Identifiers

PMID39386334
PMCPMC11459284

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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.