Evidence map›Paper›PMID 35873514›Full record

ReviewDEN open2022

Background features in the cytology of pancreatic neoplasms.

Kenichi Hirabayashi, Tsubasa Saika, Naoya Nakamura

Open access · goldAbstract readReview
In one paragraph

Review in DEN open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 35% of its field
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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Review
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 at 2 institutions in 1 country.

Kenichi HirabayashiDepartment of Pathology Tokai University School of Medicine Kanagawa Japan.ORCID https://orcid.org/0000-0002-9516-1792
Tsubasa SaikaDiagnostic Pathology Center Tokai University Hospital Kanagawa Japan.
Naoya NakamuraDepartment of Pathology Tokai University School of Medicine Kanagawa Japan.
Tokai University · JPTokai University Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cytology is a useful method for diagnosing pancreatic neoplasms. Although endoscopic ultrasound-guided fine-needle aspiration has recently become the mainstream method for the diagnosis of pancreatic neoplasms, pancreatic juice and pancreatic duct brushing cytology continue to be useful diagnostic methods for the investigation of pancreatic neoplasms. Diagnoses using pancreatic cytology are primarily based on the features related to tumor cells; however, evaluation of the background features provides important information that could further aid the diagnosis. Pancreatic neoplasms show various histological types, each of which is associated with its own characteristic background features. The necrotic background, desmoplastic stroma, and presence of cancer-associated fibroblasts are background features of pancreatic ductal adenocarcinoma, a mucinous background is associated with intraductal papillary mucinous neoplasms and mucinous cystic neoplasms, and hyaline globules are observed in solid pseudopapillary neoplasms. However, some background features are associated with more than one histological type of pancreatic neoplasm, highlighting the importance to base a diagnosis on the results of a comprehensive analysis of not only the background features but also the tumor cells. Here, we provide a review of the key background cytological features of pancreatic neoplasms, which can serve as a guide to improve diagnosis and research.

Indexed as

background featurecytologymucinnecrosispancreatic neoplasm

Identifiers

PMID35873514
PMCPMC9302047
OpenAlexW4220991543

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.