ReviewCancers2023
Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma.
Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025Pooled it
- Common cystic pancreatic neoplasms: A comprehensive magnetic resonance imaging-based review of typical and atypical imaging features.World journal of radiology · 2026Review
- Radiomics and artificial intelligence in pancreatic cyst characterization: future or fiction?Abdominal radiology (New York) · 2026Review
- A nomogram for malignancy prediction of pancreatic cystic lesions based on trans-abdominal ultrasound features.BMC medical imaging · 2026Article
- Pancreatic Cystic Lesions and Endoscopic Ultrasound Diagnostic Equipment: A Literature Review.Journal of clinical medicine · 2026Review
- Development and validation of a CT-radiomics model for the diagnosis of lung cancer associated with cystic airspaces: a multicenter study.BMC cancer · 2025Article
- Applications of artificial intelligence in abdominal imaging.Abdominal radiology (New York) · 2025Review
- Artificial Intelligence in Pancreatobiliary Endoscopy: Current Advances, Opportunities, and Challenges.Journal of clinical medicine · 2025Review
- Importance of landscape exploration and progress in molecular therapies and precision medicine for pancreatic ductal adenocarcinoma.World journal of gastrointestinal oncology · 2025Review
- A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images.BMC medical imaging · 2025Article
- Review
- Advances in Endoscopic Ultrasound in Pancreatic Cancer Screening, Diagnosis, and Palliative Care.Biomedicines · 2024Review
- Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations.Cancers · 2024Review
- Emerging trends and hotspots in cervical intraepithelial neoplasia research from 2013 to 2023: A bibliometric analysis.Heliyon · 2024Article
- Basic Principles and Role of Endoscopic Ultrasound in Diagnosis and Differentiation of Pancreatic Cancer from Other Pancreatic Lesions: A Comprehensive Review of Endoscopic Ultrasound for Pancreatic Cancer.Journal of clinical medicine · 2024Review
- Molecular Pathology of Pancreatic Cystic Lesions with a Focus on Malignant Progression.Cancers · 2024Review
- Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images.Diagnostics (Basel, Switzerland) · 2024Review
- MED-ChatGPT CoPilot: a ChatGPT medical assistant for case mining and adjunctive therapy.Frontiers in medicine · 2024Article
- Knowledge, Attitude and Practice of Radiologists Regarding Artificial Intelligence in Medical Imaging.Journal of multidisciplinary healthcare · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65-75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.
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.