ArticleBMC medical research methodology2024
Predicting lung cancer survival prognosis based on the conditional survival bayesian network.
Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- A multi-omics integration of bulk and single-cell transcriptomics identifies and validates a 16-gene epithelial-mesenchymal transition prognostic signature in tongue squamous cell carcinoma.Translational cancer research · 2026Article
- Causal analysis of traditional and environmental risk factors for long-term development of type 2 diabetes using a conditional survival Bayesian network: evidence from the Korean Genome and Epidemiology Study.BMC public health · 2026Article
- Bayesian networks as prognostic models in oncology: a systematic review and recommendations for clinical practice.BMJ oncology · 2026Article
- Bayesian Model Prediction for Breast Cancer Survival: A Retrospective Analysis.European journal of breast health · 2025Article
- Comparative evaluation of negative lymph node count, positive lymph node count, and lymph node ratio in prognostication of survival following completely resection for non-small cell lung cancer: a multicenter population-based analysis.Frontiers in surgery · 2024Article
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Authors and funding
24 authors at 5 institutions in 1 country.
Funding
Abstract
Lung cancer is a leading cause of cancer deaths and imposes an enormous economic burden on patients. It is important to develop an accurate risk assessment model to determine the appropriate treatment for patients after an initial lung cancer diagnosis. The Cox proportional hazards model is mainly employed in survival analysis. However, real-world medical data are usually incomplete, posing a great challenge to the application of this model. Commonly used imputation methods cannot achieve sufficient accuracy when data are missing, so we investigated novel methods for the development of clinical prediction models. In this article, we present a novel model for survival prediction in missing scenarios. We collected data from 5,240 patients diagnosed with lung cancer at the Weihai Municipal Hospital, China. Then, we applied a joint model that combined a BN and a Cox model to predict mortality risk in individual patients with lung cancer. The established prognostic model achieved good predictive performance in discrimination and calibration. We showed that combining the BN with the Cox proportional hazards model is highly beneficial and provides a more efficient tool for risk prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.