ArticleJournal of translational medicine2021
Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection.
Article in Journal of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- A systematic review of predictive models for hospital-acquired pressure injury using machine learning.Nursing open · 2023Pooled it
- The role of artificial intelligence in pancreatic surgery: a systematic review.Updates in surgery · 2022Pooled it
- Validation is not enough: Longitudinal evidence of post-deployment fragility in clinical AI systems.PLOS digital health · 2026Article
- AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication.Abdominal radiology (New York) · 2025Review
- Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.BMC medical informatics and decision making · 2025Article
- Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.Cancer letters · 2025Review
- Performance of different CT enhancement quantification methods as predictors of pancreatic cancer recurrence after upfront surgery.Scientific reports · 2024Article
- Comprehensive Analysis of the Function and Prognostic Value of TAS2Rs Family-Related Genes in Colon Cancer.International journal of molecular sciences · 2024Article
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- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- New model to predict survival in advanced pancreatic ductal adenocarcinoma patients by measuring GGT and LDH levels and monocyte count.Frontiers in oncology · 2024Article
- Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study.Frontiers in digital health · 2024Article
- The Utility of Artificial Intelligence in the Diagnosis and Management of Pancreatic Cancer.Cureus · 2023Review
- A clinical decision support system to predict the efficacy for EGFR-TKIs based on artificial neural network.Journal of cancer research and clinical oncology · 2023Article
- Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery.Surgery today · 2023Article
- Machine learning in pancreas surgery, what is new? literature review.Frontiers in surgery · 2023Review
- A Novel Small Molecular Prostaglandin Receptor EP4 Antagonist, L001, Suppresses Pancreatic Cancer Metastasis.Molecules (Basel, Switzerland) · 2022Article
- Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Artificial intelligence in pancreatic cancer.Theranostics · 2022Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSurgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC) and the survival of patients after radical resection is closely related to relapse. We aimed to develop models to predict the risk of relapse using machine learning methods based on multiple clinical parameters.
methodsData were collected and analysed of 262 PDAC patients who underwent radical resection at 3 institutions between 2013 and 2017, with 183 from one institution as a training set, 79 from the other 2 institution as a validation set. We developed and compared several predictive models to predict 1- and 2-year relapse risk using machine learning approaches.
resultsMachine learning techniques were superior to conventional regression-based analyses in predicting risk of relapse of PDAC after radical resection. Among them, the random forest (RF) outperformed other methods in the training set. The highest accuracy and area under the receiver operating characteristic curve (AUROC) for predicting 1-year relapse risk with RF were 78.4% and 0.834, respectively, and for 2-year relapse risk were 95.1% and 0.998. However, the support vector machine (SVM) model showed better performance than the others for predicting 1-year relapse risk in the validation set. And the k neighbor algorithm (KNN) model achieved the highest accuracy and AUROC for predicting 2-year relapse risk.
conclusionsBy machine learning, this study has developed and validated comprehensive models integrating clinicopathological characteristics to predict the relapse risk of PDAC after radical resection which will guide the development of personalized surveillance programs after surgery.
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