Bayesian Inference of Lymph Node Ratio Estimation and Survival Prognosis for Breast Cancer Patients

Jing Teng, Assem Abdygametova, Jing Du, Bian Ma, Rong Zhou, Yu Shyr, Fei Ye

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Objective: We evaluated the prognostic value of lymph node ratio (LNR) for the survival of breast cancer patients using Bayesian inference. Methods: Data on 5,279 women with infiltrating duct and lobular carcinoma breast cancer, diagnosed from 2006-2010, was obtained from the NCI SEER Cancer Registry. A prognostic modeling framework was proposed using Bayesian inference to estimate the impact of LNR in breast cancer survival. Based on the proposed model, we then developed a web application for estimating LNR and predicting overall survival. Results: The final survival model with LNR outperformed the other models considered (C-statistic 0.71). Compared to directly measured LNR, estimated LNR slightly increased the accuracy of the prognostic model. Model diagnostics and predictive performance confirmed the effectiveness of Bayesian modeling and the prognostic value of the LNR in predicting breast cancer survival. Conclusion: The estimated LNR was found to have a significant predictive value for the overall survival of breast cancer patients.Significance: We used Bayesian inference to estimate LNR which was then used to predict overall survival. The models were developed from a large population-based cancer registry. We also built a user-friendly web application for individual patient survival prognosis. The diagnostic value of the LNR and the effectiveness of the proposed model were evaluated by comparisons with existing prediction models.

Original languageEnglish
Article number8847382
Pages (from-to)354-364
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Volume24
Issue number2
DOIs
Publication statusPublished - 2020 Feb

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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