Evidence map›Paper›PMID 40394929›Full record

ReviewClinical endoscopy2025

Future of image enhanced endoscopy of esophageal adenocarcinoma.

Kerem Parlar, Mert Cakir, Ozlem Ozer, Prateek Sharma

Abstract readReview
In one paragraph

Review in Clinical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Kerem ParlarDepartment of Internal Medicine, Istanbul University Cerrahpasa Faculty of Medicine, Istanbul, Turkey.
Mert CakirDivision of Gastroenterology and Hepatology, Department of Internal Medicine, Istanbul Demiroglu Bilim University Faculty of Medicine, Istanbul, Turkey.
Ozlem OzerDivision of Gastroenterology and Hepatology, Department of Internal Medicine, Istanbul Demiroglu Bilim University Faculty of Medicine, Istanbul, Turkey.
Prateek SharmaDivision of Gastroenterology and Hepatology, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Barrett's esophagus is a premalignant precursor lesion of esophageal adenocarcinoma that affects approximately 1% of the population worldwide. Esophageal adenocarcinoma has a high mortality rate with a five-year survival of 15% to 20%. Early detection of Barrett's esophagus and dysplasia via endoscopy is crucial for preventing its progression to esophageal adenocarcinoma. New imaging techniques, such as image-enhanced endoscopy, have simplified the identification of Barrett's esophagus, dysplasia, and esophageal adenocarcinoma. Narrow-band imaging, blue-light imaging, and i-Scan are the prominent image-enhanced endoscopic techniques used to detect neoplasia. In Barrett's screening and surveillance, key aspects such as the screening population, tools, and intervals need to be clearly defined and standardized for future guidelines to improve the detection of precursor lesions and reduce the incidence of esophageal adenocarcinoma. Making image-enhanced endoscopy less subjective and enhancing the quality measures during endoscopy are crucial steps. Examples of quality measures include cleaning the esophagus before endoscopy and allowing sufficient time for inspection. Artificial intelligence systems can aid the early identification of lesions and reduce subjectivity.

Indexed as

Artificial intelligenceBarrett esophagusEsophageal neoplasmsImage-enhanced endoscopyUpper gastrointestinal endoscopy

Identifiers

PMID40394929
PMCPMC12314617

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.