Pancreatic Cancer Prediction Through an Artificial Neural Network
Open accessAbstract
Early detection of pancreatic cancer is challenging because cancer-specific symptoms occur only at an advanced stage, and a reliable screening tool to identify high-risk patients is lacking. To address this challenge, an artificial neural network (ANN) was developed, trained, and tested using the health data of 800,114 respondents captured in the National Health Interview Survey (NHIS) and Pancreatic, Lung, Colorectal, and Ovarian cancer (PLCO) datasets, together containing 898 patients diagnosed with pancreatic cancer. Prediction of pancreatic cancer risk was assessed at an individual level by incorporating 18 features into the neural network. The established ANN model achieved a sensitivity of 87.3 and 80.7%, a specificity of 80.8 and 80.7%, and an area under the receiver operating characteristic curve of 0.86 and 0.85 for the training and testing cohorts, respectively. These results indicate that our ANN can be used to predict pancreatic cancer risk with high discriminatory power and may provide a novel approach to identify patients at higher risk for pancreatic cancer who may benefit from more tailored screening and intervention.
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An artificial neural network model using US National Health Interview Survey and PLCO Trial data reached an AUC of 0.85 and included age, sex, race and ethnicity, diabetes, emphysema, asthma, stroke, cardiovascular diseases, ulcers, other cancer, hypertension, smoking status, physical activity, alcohol consumption, and family history of pancreatic ductal adenocarcinoma.
“This model reached an AUC of 0·85 and incorporated data on age, sex, race and ethnicity, diabetes, emphysema, asthma, stroke, cardiovascular diseases, ulcers, other cancer, hypertension, smoking status, physical activity, alcohol consumption, and family history of pancreatic ductal adenocarcinoma.”
From Early detection of pancreatic cancer (2020) · cites this paper as [109]
Muhammad and colleagues used artificial intelligence to analyse personal health data to calculate pancreatic ductal adenocarcinoma risk in the general population and identify high-risk individuals.
“Muhammad and colleagues used artificial intelligence to analyse available personal health data to calculate risk for pancreatic ductal adenocarcinoma in the general population and to identify high-risk individuals.109”
From Early detection of pancreatic cancer (2020) · cites this paper as [109]
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- Authors
- Muhammad, W., Hart, G. R., Nartowt, B., Farrell, J. J., Johung, K., Liang, Y., & Deng, J.
- Affiliations
- Yale University (US)
- Corresponding author
- Deng, Jun
- ORCID
- Venue
- Frontiers in Artificial Intelligence
- Publisher
- Frontiers Media SA
- Published
- 2019-05-03
- Volume
- 2
- Pages
- 2-2
- ISSN
- 2624-8212 (ISSN-L)
- PMC
- PMC7861334
- OpenAlex
- W2943347371
- Cited by
- 159
- References
- 58
- Citations by year
- 2026: 7 · 2025: 28 · 2024: 26 · 2023: 39 · 2022: 24 · 2021: 21 · 2020: 13 · 2019: 1
- FWCI
- 16.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Artificial Intelligence (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Yale University
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
- Keywords
- pancreatic cancer, cancer risk prediction, artificial neural networks, early detection, cancer screening, high-risk patient identification, National Health Interview Survey
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