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Master of Public Health

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

AUTHORS

Teng J , Abdygametova A , Du J , Ma B , Zhou R , Shyr Y , Ye F , . IEEE journal of biomedical and health informatics. 2019 9 24; ().

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 per- formance 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.

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