ArticleCancers2022
The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of machine learning methods versus traditional Cox regression for survival prediction in cancer using real-world data: a systematic literature review and meta-analysis.BMC medical research methodology · 2025Pooled it
- Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Multiple prognostic prediction models for subglottic laryngeal cancer based on machine learning.Translational cancer research · 2026Article
- Risk-dependent conditional survival analysis and annual hazard rate of primary ocular adnexal lymphoma.Discover oncology · 2026Article
- Explainable artificial intelligence for personalized prognosis in pancreatic cancer: A nationwide study from Taiwan.PLOS digital health · 2026Article
- Prognostic nomograms for overall and cancer-specific survival in individuals with large cell neuroendocrine carcinoma: a population-based study.Translational cancer research · 2026Article
- Risk factors associated with overall survival in patients with cervical cancer: a prospective cohort study in Western China comparing random survival forest and Cox proportional hazards models.Journal of gynecologic oncology · 2026Article
- Factors associated with psychological distress among family caregivers of preschool children with autism: an analysis.Frontiers in psychiatry · 2026Article
- Multimodal prediction models integrating radiomics and three-dimensional deep learning for acute respiratory distress syndrome in acute pancreatitis patients.The Journal of international medical research · 2026Article
- Establishing and validating models integrated with hematological biomarkers and clinical characteristics for the prognosis of non-esophageal squamous cell carcinoma patients.Annals of medicine · 2025Article
- Prediction of spontaneous preterm birth in pregnant women using machine learning.Archives of gynecology and obstetrics · 2025Article
- Machine learning survival models for Non-alcoholic fatty liver disease based on a health checkup cohort.BMC gastroenterology · 2025Article
- Comparison of Random Survival Forest Based-Overall Survival With Deep Learning and Cox Proportional Hazard Models in HER-2-Positive HR-Negative Breast Cancer.Cancer reports (Hoboken, N.J.) · 2025Article
- Using Machine Learning to Predict MACEs Risk in Patients with Premature Myocardial Infarction.Reviews in cardiovascular medicine · 2025Article
- Prediction of mortality in patients with retinoblastoma based on random survival forest: a retrospective cohort analysis using SEER database.Translational cancer research · 2025Article
- Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.Scientific reports · 2025Article
- Comprehensive Machine Learning-Based Prediction Model for Delirium Risk in Older Patients with Dementia: Risk Factors Identification.Clinical interventions in aging · 2025Article
- Article
- Construction of a combined prognostic model for pancreatic ductal adenocarcinoma based on deep learning and digital pathology images.BMC gastroenterology · 2024Article
- Artificial intelligence methods available for cancer research.Frontiers of medicine · 2024Review
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8 authors.
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Abstract
Accurate prediction for the prognosis of patients with pancreatic cancer (PC) is a emerge task nowadays. We aimed to develop survival models for postoperative PC patients, based on a novel algorithm, random survival forest (RSF), traditional Cox regression and neural networks (Deepsurv), using the Surveillance, Epidemiology, and End Results Program (SEER) database. A total of 3988 patients were included in this study. Eight clinicopathological features were selected using least absolute shrinkage and selection operator (LASSO) regression analysis and were utilized to develop the RSF model. The model was evaluated based on three dimensions: discrimination, calibration, and clinical benefit. It found that the RSF model predicted the cancer-specific survival (CSS) of the postoperative PC patients with a c-index of 0.723, which was higher than the models built by Cox regression (0.670) and Deepsurv (0.700). The Brier scores at 1, 3, and 5 years (0.188, 0.177, and 0.131) of the RSF model demonstrated the model's favorable calibration and the decision curve analysis illustrated the model's value of clinical implement. Moreover, the roles of the key variables were visualized in the Shapley Additive Explanations plotting. Lastly, the prediction model demonstrates value in risk stratification and individual prognosis. In this study, a high-performance prediction model for PC postoperative prognosis was developed, based on RSF The model presented significant strengths in the risk stratification and individual prognosis prediction.
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