ArticlePancreas2020
Prediction of Pancreatic Cancer Based on Imaging Features in Patients With Duct Abnormalities.
Article in Pancreas, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.The American journal of gastroenterology · 2024Pooled it
- Non-invasive Assessment of Pancreatic Duct Hypertension Using Computational Flow Modeling.Annals of biomedical engineering · 2026Article
- Integrating Radiomics and Computational Pathology to Predict Early Recurrence of Pancreatic Ductal Adenocarcinoma and Uncover Its Biological Basis in Tumor Microenvironment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Predictive imaging in abdominal oncology: current trends and future directions.Japanese journal of radiology · 2026Review
- Article
- Review
- Identification of pancreatic cancer risk factors from clinical notes using natural language processing.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2024Article
- From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer.Diagnostics (Basel, Switzerland) · 2024Review
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis.World journal of gastroenterology · 2023Review
- Potential of artificial intelligence in the risk stratification for and early detection of pancreatic cancer.Artificial intelligence surgery · 2023Article
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- Artificial intelligence and imaging for risk prediction of pancreatic cancer: a narrative review.Chinese clinical oncology · 2022Review
- Clinical and Imaging Predictors of Pancreatic Cancer in Patients Hospitalized for Acute Pancreatitis.Gastro hep advances · 2022Article
- Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review.Pancreas · 2021Review
- Screening for pancreatic cancer has the potential to save lives, but is it practical?Expert review of gastroenterology & hepatologyReview
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesAbnormalities of the main pancreatic duct may be an early indicator of pancreatic ductal adenocarcinoma (PDAC). We develop and validate algorithms that predict the risk of PDAC using features identified on cross-sectional imaging and other clinical characteristics collected through electronic medical records.
methodsAdult patients with abdominal computed tomography or magnetic resonance imaging in January 2006 to June 2016 demonstrating dilatation of main pancreatic duct were identified. Pancreas-related morphologic features were extracted from radiology reports using natural language processing. The cumulative incidence of PDAC with death as a competing risk was estimated using multistate models. Model discrimination was assessed using c-index. The models were internally validated using bootstrapping.
resultsThe cohort consisted of 7819 patients (mean age, 71 years; 65% female). A total of 781 patients (10%) developed PDAC within 3 years after the first eligible imaging study. The final models achieved reasonable discrimination (c-index, 0.825-0.833). The 3-year average risk of PDAC in the top 5% of the total eligible patients was 56.0%, more than 20 times of the average risk among the bottom 50% of patients.
conclusionsPrediction models combining imaging features and clinical measures can be used to further stratify the risk of pancreatic cancer among patients with pancreas ductal dilatation.
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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.