Evidence map›Paper›PMID 37371782›Full record

ReviewBiomedicines2023

Advances in the Early Diagnosis of Pancreatic Ductal Adenocarcinoma and Premalignant Pancreatic Lesions.

Reiko Yamada, Junya Tsuboi, Yumi Murashima, Takamitsu Tanaka, Kenji Nose, Hayato Nakagawa

Open access · goldAbstract readReview
In one paragraph

Review in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.8field-weighted citation impact, top 13% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Artificial intelligence in pancreatic cancer: applications in early detection, tumor staging, and survival prediction-a comprehensive review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
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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

6 authors at 1 institution in 1 country.

Reiko YamadaDepartment of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan.ORCID 0000-0003-2318-073X
Junya TsuboiDepartment of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan.
Yumi MurashimaDepartment of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan.
Takamitsu TanakaDepartment of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan.
Kenji NoseDepartment of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan.
Hayato NakagawaDepartment of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan.
Mie University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pancreatic cancer is one of the most lethal human malignancies, in part because it is often diagnosed at late stages when surgery and systemic therapies are either unfeasible or ineffective. Therefore, diagnosing pancreatic cancer in earlier stages is important for effective treatment. However, because the signs and symptoms may be nonspecific and not apparent until the disease is at a late stage, the timely diagnoses of pancreatic cancer can be difficult to achieve. Recent studies have shown that selective screening and increased usage of biomarkers could improve the early diagnosis of pancreatic cancer. In this review, we discuss recent advancements in the early detection of pancreatic ductal carcinoma and precancerous lesions. These include innovations in imaging modalities, the diagnostic utility of various biomarkers, biopsy techniques, and population-based surveillance approaches. Additionally, we discuss how machine learning methods are being applied to develop integrated methods of identifying individuals at high risk of developing pancreatic disease. In the future, the overall survival of pancreatic cancer patients could be improved by the development and adoption of these new methods and techniques.

Indexed as

biomarkersdiagnosticsEUSEUS-FNAEUS-FNBhereditary conditionspancreatic cancer

Identifiers

PMID37371782
PMCPMC10296512
OpenAlexW4380360327

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.