ArticleJournal of cancer research and clinical oncology2023
Machine learning was used to predict risk factors for distant metastasis of pancreatic cancer and prognosis analysis.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- [Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Development and validation of machine learning nomograms for predicting survival in stage IV pancreatic cancer: A retrospective study.World journal of gastrointestinal oncology · 2025Article
- Combining Clinical-Radiomics Features With Machine Learning Methods for Building Models to Predict Postoperative Recurrence in Patients With Chronic Subdural Hematoma: Retrospective Cohort Study.Journal of medical Internet research · 2024Article
- A machine learning-based nomogram model for predicting the recurrence of cystitis glandularis.Therapeutic advances in urologyArticle
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7 authors.
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Abstract
backgroundThe mechanisms of distant metastasis in pancreatic cancer (PC) have not been elucidated, and this study aimed to explore the risk factors affecting the metastasis and prognosis of metastatic patients and to develop a predictive model.
methodClinical data from patients meeting criteria from 1990 to 2019 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and two machine learning methods, random forest and support vector machine, combined with logistic regression, were used to explore risk factors influencing distant metastasis and to create nomograms. The performance of the model was validated using calibration curves and ROC curves based on the Shaanxi Provincial People's Hospital cohort. LASSO regression and Cox regression models were used to explore the independent risk factors affecting the prognosis of patients with distant PC metastases.
resultsWe found that independent risk factors affecting PC distant metastasis were: age, radiotherapy, chemotherapy, T and N; the independent risk factors for patient prognosis were: age, grade, bone metastasis, brain metastasis, lung metastasis, radiotherapy and chemotherapy.
conclusionTogether, our study provides a method for risk factors and prognostic assessment for patients with distant PC metastases. The nomogram we developed can be used as a convenient individualized tool to facilitate aid in clinical decision making.
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