Evidence map›Paper›PMID 39467913›Full record

ReviewAbdominal radiology (New York)2025

Advancements in early detection of pancreatic cancer: the role of artificial intelligence and novel imaging techniques.

Chenchan Huang, Yiqiu Shen, Samuel J Galgano, Ajit H Goenka, Elizabeth M Hecht, Avinash Kambadakone, Zhen Jane Wang, Linda C Chu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Explainable Lightweight Model Using Low-Rank and Convolutional Block Attention for Pancreatic Cancer Diagnosis.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026
    Article
  3. Article
  4. Pancreatic cancer in 2025: Have we found a solution?World journal of gastroenterology · 2025
    Review
  5. Article
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

8 authors.

Chenchan HuangNew York University Langone Health, New York, USA. chenchan.huang@nyulangone.org.
Yiqiu ShenNew York University Langone Health, New York, USA.
Samuel J GalganoUniversity of Alabama at Birmingham, Birmingham, USA.
Ajit H GoenkaMayo Clinic, Rochester, USA.
Elizabeth M HechtWeill Cornell Medicine, New York, USA.
Avinash KambadakoneMassachusetts General Hospital, Boston, USA.
Zhen Jane WangUniversity of California, San Francisco, San Francisco, USA.
Linda C ChuJohns Hopkins University School of Medicine, Baltimore, USA.

Funding

Optimizing Pancreatic Cancer Management with Next Generation Imaging and Liquid BiopsyR01CA256969 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Eric Collisson, Ajit Harishkumar Goenka · 2021 to 2026
$3.1M
Quantitative In Vivo 68Ga-Fibroblast-Activation-Protein-Inhibitors (FAPI)-46 PET Imaging of Cancer-Associated Fibroblasts (CAFs) in Pancreatic Ductal Adenocarcinoma (PDA)R01CA272628 · NCI · MAYO CLINIC ROCHESTER · PI GOENKA, AJIT HARISHKUMAR · 2022 to 2025
$2.5M
NCI NIH HHS R01 CA256969NCI NIH HHS R01 CA272628
6 · The paper itself

Abstract

Early detection is crucial for improving survival rates of pancreatic ductal adenocarcinoma (PDA), yet current diagnostic methods can often fail at this stage. Recently, there has been significant interest in improving risk stratification and developing imaging biomarkers, through novel imaging techniques, and most notably, artificial intelligence (AI) technology. This review provides an overview of these advancements, with a focus on deep learning methods for early detection of PDA.

Indexed as

Artificial IntelligenceCarcinoma, Pancreatic DuctalEarly Detection of CancerPancreatic NeoplasmsDeep LearningHumansImage Interpretation, Computer-AssistedArtificial intelligenceEarly detectionNanoparticle contrast agentPancreatic cancerPhoton-counting CT

Identifiers

PMID39467913
PMCPMC12282273

What OpenQuestion holds

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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.