Prediction of Pancreatic Cancer Based on Imaging Features in Patients With Duct Abnormalities
Open accessAbstract
Objectives: Abnormalities 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. Methods: Adult 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. Results: The 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. Conclusions: Prediction 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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Wansu Chen and Bechien U. Wu have previously reported work using natural language processing to identify individuals at risk for pancreatic cancer based on radiology reports.
“Wansu Chen and Bechien U. Wu of the Kaiser Permanente Southern California have previously reported work using natural language processing to identify individuals at risk for pancreatic cancer based on radiology reports.159”
From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [159]
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- Authors
- Chen, W., Butler, R. K., Zhou, Y., Parker, R. A., Jeon, C. Y., & Wu, B. U.
- Affiliations
- Kaiser Permanente (US)
- Los Angeles Medical Center (US)
- Cedars-Sinai Medical Center (US)
- Corresponding author
- Chen, Wansu
- Butler, Rebecca K.
- Zhou, Yichen
- Venue
- Pancreas
- Published
- 2020-03
- Volume
- 49
- Issue
- 3
- Pages
- 413-419
- ISSN
- 0885-3177 (ISSN-L)
- 1536-4828
- PMC
- PMC7444654
- OpenAlex
- W3010821239
- Cited by
- 27
- References
- 26
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 7 · 2023: 5 · 2022: 6 · 2021: 1 · 2020: 1
- FWCI
- 1.17
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Pancreas (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (U01DK108314)
- Topics
- Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Surgery › Pancreatitis Pathology and Treatment
- Keywords
- pancreatic ductal adenocarcinoma, pancreatic duct dilatation, multi-state models, competing risk of death, risk stratification, computed tomography, magnetic resonance imaging
- MeSH terms
- Data Warehousing, Aged, Aged, 80 and over, California, Dilatation, Pathologic, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Pancreatic Ducts, Pancreatic Neoplasms, Predictive Value of Tests, Prognosis, Retrospective Studies, Risk Factors, Tomography, X-Ray Computed, Reproducibility of Results, Incidence, Risk Assessment, Carcinoma, Pancreatic Ductal
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