ReviewBiomimetics (Basel, Switzerland)2022
Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions.
Review in Biomimetics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07543263 (An Artificial Intelligence System for Multimodal, Multi-class Diagnosis of Pancreatic Cystic Lesions Based on Endoscopic Ultrasonography), which is not on this map. Cited by 8 papers.
What it found
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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.
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.
An Artificial Intelligence System for Multimodal, Multi-class Diagnosis of Pancreatic Cystic Lesions Based on Endoscopic Ultrasonography
Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- Deep learning-based surgical phase recognition in laparoscopic cholecystectomy.Annals of hepato-biliary-pancreatic surgery · 2024Article
- Review
- Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy.Biomimetics (Basel, Switzerland) · 2023Review
- Deep-Learning-Enabled Computer-Aided Diagnosis in the Classification of Pancreatic Cystic Lesions on Confocal Laser Endomicroscopy.Diagnostics (Basel, Switzerland) · 2023Article
- Role of Endoscopic Ultrasound in the Evaluation of Pancreatic Cystic Neoplasms: A Concise Review.Diagnostics (Basel, Switzerland) · 2023Review
- The Role of Magnetic Resonance Imaging (MRI) in the Diagnosis of Pancreatic Cystic Lesions (PCLs).Diagnostics (Basel, Switzerland) · 2023Review
- Modern aspects of the management of pancreatic intraductal papillary mucinous neoplasms: a narrative review.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rate of incidentally detected pancreatic cystic lesions (PCLs) has increased over the past decade and was recently reported at 8%. These lesions pose a unique challenge, as each subtype of PCL carries a different risk of malignant transformation, ranging from 0% (pancreatic pseudocyst) to 34-68% (main duct intraductal papillary mucinous neoplasm). It is imperative to correctly risk-stratify the malignant potential of these lesions in order to provide the correct care course for the patient, ranging from monitoring to surgical intervention. Even with the multiplicity of guidelines (i.e., the American Gastroenterology Association guidelines and Fukuoka/International Consensus guidelines) and multitude of diagnostic information, risk stratification of PCLs falls short. Studies have reported that 25-64% of patients undergoing PCL resection have pancreatic cysts with no malignant potential, and up to 78% of mucin-producing cysts resected harbor no malignant potential on pathological evaluation. Clinicians are now incorporating artificial intelligence technology to aid in the management of these difficult lesions. This review article focuses on advancements in artificial intelligence within digital pathomics, radiomics, and genomics as they apply to the diagnosis and risk stratification of PCLs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.