ArticlePloS one2016
Development and Validation of a Prediction Model to Estimate Individual Risk of Pancreatic Cancer.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Leveraging the Electronic Health Record for Early Detection of Pancreatic Cancer Among 9.4 Million US Veterans.Clinical and translational gastroenterology · 2026Article
- A Simple Clinical Scoring System to Determine the Risk of Pancreatic Cancer in the General Population.Diagnostics (Basel, Switzerland) · 2024Article
- An Integrative Pancreatic Cancer Risk Prediction Model in the UK Biobank.Biomedicines · 2023Article
- Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks.Journal of biomedical informatics · 2021Article
- Early detection of pancreatic cancer.The lancet. Gastroenterology & hepatology · 2020Review
- Prediction of Pancreatic Cancer Based on Imaging Features in Patients With Duct Abnormalities.Pancreas · 2020Article
- Development of a stratification tool to identify pancreatic intraductal papillary mucinous neoplasms at lowest risk of progression.Alimentary pharmacology & therapeutics · 2019Article
- Environmental Risk Factors of Pancreatic Cancer.Journal of clinical medicine · 2019Review
- A review of lifestyle, metabolic risk factors, and blood-based biomarkers for early diagnosis of pancreatic ductal adenocarcinoma.Journal of gastroenterology and hepatology · 2019Review
- Pancreatic Cancer Prediction Through an Artificial Neural Network.Frontiers in artificial intelligence · 2019Article
- Article
- Improving risk prediction for pancreatic cancer in symptomatic patients: a Saudi Arabian study.Cancer management and research · 2018Article
- Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThere is no reliable screening tool to identify people with high risk of developing pancreatic cancer even though pancreatic cancer represents the fifth-leading cause of cancer-related death in Korea. The goal of this study was to develop an individualized risk prediction model that can be used to screen for asymptomatic pancreatic cancer in Korean men and women. MATERIALS AND
methodsGender-specific risk prediction models for pancreatic cancer were developed using the Cox proportional hazards model based on an 8-year follow-up of a cohort study of 1,289,933 men and 557,701 women in Korea who had biennial examinations in 1996-1997. The performance of the models was evaluated with respect to their discrimination and calibration ability based on the C-statistic and Hosmer-Lemeshow type χ2 statistic.
resultsA total of 1,634 (0.13%) men and 561 (0.10%) women were newly diagnosed with pancreatic cancer. Age, height, BMI, fasting glucose, urine glucose, smoking, and age at smoking initiation were included in the risk prediction model for men. Height, BMI, fasting glucose, urine glucose, smoking, and drinking habit were included in the risk prediction model for women. Smoking was the most significant risk factor for developing pancreatic cancer in both men and women. The risk prediction model exhibited good discrimination and calibration ability, and in external validation it had excellent prediction ability.
conclusionGender-specific risk prediction models for pancreatic cancer were developed and validated for the first time. The prediction models will be a useful tool for detecting high-risk individuals who may benefit from increased surveillance for pancreatic cancer.
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