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. 2023 Sep 21;9(10):e20360.
doi: 10.1016/j.heliyon.2023.e20360. eCollection 2023 Oct.

Survival analysis of recurrent breast cancer patients using mix Bayesian network

Parviz Shahmirzalou  1 , Majid Jafari Khaledi  2 , Maryam Khayamzadeh  3 , Aliakbar Rasekhi  1
Affiliations

Survival analysis of recurrent breast cancer patients using mix Bayesian network

Parviz Shahmirzalou et al. Heliyon. .

Abstract

Introduction: Breast cancer (BC) is the most common cancer among women. Iranians have an 11% BC recurrence rate, which lowers their survival rates. Few studies have investigated cancer recurrence survival rates. This study's major purpose is to use a mixed Bayesian network (BN) to analyze recurrent patients' survival.

Material and methods: This study aimed to evaluate the pathobiological features, age, gender, final status, and survival time of the patients. Bayesian imputation was used for missing data. The performance of BN was optimized through the utilization of a blacklist and prior probability. After structural and parametric learning, posterior conditional probabilities and mean survival periods for the node arcs were predicted. The hold-out technique based on the posterior classification error was used to investigate the model's validation.

Results: The study included 220 cancer recurrence patients. These patients averaged 47 years old. The BN with a blacklist and prior probability has a higher network score than other networks. The hold-out technique verified structural learning. The Directed Acyclic Graph showed a statistically significant relationship between cancer biomarkers (ER, PR, and HER2 receptors), cancer stage, and tumor grade and patient survival duration. Patient death was also significantly associated with education, ER, PR, HER2, and tumor grade. The BN reports that HER2 negative, ER positive, and PR positive patients had a higher survival rate.

Conclusion: Survival and death of relapsed patients depend on biomarkers. Based on the findings, patient survival can be predicted with their features.

Keywords: Breast cancer; Directed acyclic graph; Mixed Bayesian network; R package deal; Recurrence.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Steps to prepare dataset and run BN on BC dataset.
Fig. 2
Fig. 2
Kaplan-Meier Survival plot for Patients with Recurrence.
Fig. 3
Fig. 3
Kaplan-Meier survival plots adjusted to study Covariates (A: Stage of Cancer, B: Grade of tumor, C: Estrogen receptor, D: Progesterone receptor, E: HER2, F: Education level).
Fig. 4
Fig. 4
The last DAG with high scores (Relative score = 1) extracted from mix BN using package deal. Discrete vertices are represented by black circles.
See this image and copyright information in PMC

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