Evidence map›Paper›PMID 40447463›Full record

ArticlePancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]2025

Towards automating risk stratification of intraductal papillary mucinous Neoplasms: Artificial intelligence advances beyond human expertise with confocal laser endomicroscopy.

Somashekar G Krishna, Ahmed Abdelbaki, Ziwei Li, Stacey Culp, Xinqi Xiong, Bertrand Napoleon, Shaffer Mok, Helga Bertani, Yunlu Feng, Pradermchai Kongkam and 15 more

Abstract read
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In one paragraph

Article in Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.], 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
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  6. 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

25 authors.

Somashekar G KrishnaDivision of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, OH, USA. Electronic address: Somashekar.krishna@osumc.edu.
Ahmed AbdelbakiDivision of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Ziwei LiDepartment of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, OH, USA.
Stacey CulpDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.
Xinqi XiongDepartment of Statistics, College of Arts and Sciences, Ohio State University, USA.
Bertrand NapoleonDepartment of Gastroenterology, Jean Mermoz Private Hospital, Ramsay Sante, France.
Shaffer MokDivision of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, FL, USA.
Helga BertaniGastroenterology and Endoscopy Unit, Azienda Ospedaliero Universitaria Policlinico di Modena, Modena, Italy.
Yunlu FengPeking Union Medical College Hospital, Dongcheng-qu, Beijing, China.
Pradermchai KongkamKing Chulalongkorn Memorial Hospital, Bangkok, Thailand.
Anjuli K LuthraDivision of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, FL, USA.
Jorge D MachicadoDivision of Gastroenterology, University of Michigan, Ann Arbor, USA.
Samer El-DikaDivision of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA.
Sarah LeblancDepartment of Gastroenterology, Jean Mermoz Private Hospital, Ramsay Sante, France.
Damien Meng Yew TanDepartment of Gastroenterology and Hepatology, Singapore General Hospital, Singapore.
Jordan BurlenDivision of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Margaret G KeaneDivision of Gastroenterology and Hepatology, Johns Hopkins University, Baltimore, MD, USA.
Tara KeihanianSection of Gastroenterology, Baylor College of Medicine, Houston, TX, USA.
Antonio Mendoza LaddDivision of Gastroenterology and Hepatology at University of California Davis, Sacramento, CA, USA.
Thiruvengadam MunirajDepartment of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA.
Kavel H VisrodiaDivision of Digestive and Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, NY, USA.
Wei ChenDepartment of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Ashwini K EsnakulaDepartment of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Phil A HartDivision of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Wei-Lun ChaoDepartment of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, OH, USA.

Funding

Endoscopic Ultrasound-guided In Vivo Confocal Laser Endomicroscopy as an Imaging Biomarker for the Accurate Risk Stratification of Intraductal Papillary Mucinous NeoplasmsR01CA279965 · NCI · OHIO STATE UNIVERSITY · PI Somashekar G. Krishna · 2023 to 2026
$1.7M
NCI NIH HHS R01 CA279965
6 · The paper itself

Abstract

BACKGROUND AND

aimsEndoscopic Ultrasound (EUS)-guided needle-based confocal laser endomicroscopy (nCLE) enables real-time microscopic visualization of pancreatic cyst epithelium and can identify high-grade dysplasia/invasive adenocarcinoma (HGD/IC) in branch-duct (BD) intraductal papillary mucinous neoplasms (IPMNs). We aimed to compare the performance of experts (humans) with artificial intelligence (AI) in stratifying dysplasia in BD-IPMNs.

methodsThis post-hoc analysis involved BD-IPMNs with definitive diagnoses from prospective EUS-nCLE studies (2015-2023) enrolled at a single center. Dysplasia grade was reviewed by two pathologists. Blinded EUS-nCLE experts reviewed unedited nCLE videos to classify dysplasia without and with revised Fukuoka criteria (revised-FC). The AI model, nCLE-AI, was similarly analyzed. Diagnostic parameters and AUC were compared to evaluate human and nCLE-AI performance.

resultsAmong 60 BD-IPMNs (mean size = 3.43 ± 1.00 cm), 23 (38.3 %) had HGD/IC. To detect HGD/IC using nCLE, interobserver agreement (IOA) among 16 nCLE experts was 'fair' (κ = 0.29, 95 % CI: 0.27-0.32), with a sensitivity of 58 %, specificity of 59 %, and AUC of 0.59 (95 % CI 0.55-0.62). Incorporating revised-FC improved the sensitivity to 72 % and AUC to 0.64 (95 % CI 0.61-0.68; p < 0.001), with similar IOA (κ = 0.36 'fair', 95 % CI: 0.33-0.38) and specificity (57 %). Comparatively, nCLE-AI achieved 87 % sensitivity, 54 % specificity, and an AUC of 0.70 (0.57-0.84). When combined with revised-FC, nCLE-AI reached 78 % sensitivity, 78 % specificity, and an AUC of 0.85 (95 % CI: 0.74-0.96, p = 0.02), significantly higher than humans with revised-FC (p < 0.01).

conclusionsHuman dysplasia classification of BD-IPMNs using nCLE showed modest IOA and accuracy. In contrast, nCLE-AI classifications combined with clinical criteria offer superior accuracy for detecting HGD/IC while eliminating interobserver variability.

Indexed as

Artificial IntelligencePancreatic Intraductal NeoplasmsPancreatic NeoplasmsAgedFemaleHumansMaleMicroscopy, ConfocalMiddle AgedRisk AssessmentArtificial intelligenceConfocal endomicroscopyEndoscopic ultrasoundIPMNPancreatic cancerPancreatic cyst

Identifiers

PMID40447463
PMCPMC12354341

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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.