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Predicting lung cancer survival prognosis based on the conditional survival bayesian network.

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.

SUBMITTER: Zhong L

PROVIDER: S-EPMC10801949 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Predicting lung cancer survival prognosis based on the conditional survival bayesian network.

Zhong Lu L Yang Fan F Sun Shanshan S Wang Lijie L Yu Hong H Nie Xiushan X Liu Ailing A Xu Ning N Zhang Lanfang L Zhang Mingjuan M Qi Yue Y Ji Huaijun H Liu Guiyuan G Zhao Huan H Jiang Yinan Y Li Jingyi J Song Chengcun C Yu Xin X Yang Liu L Yu Jinchao J Feng Hu H Guo Xiaolei X Yang Fujun F Xue Fuzhong F

BMC medical research methodology 20240122 1

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 suffic ...[more]

PMID: 38254038

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Dataset Information

Predicting lung cancer survival prognosis based on the conditional survival bayesian network.

Publications

Predicting lung cancer survival prognosis based on the conditional survival bayesian network.

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