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.
ABSTRACT
OBJECTIVES: The main objective of this study was modelling experienced caries of deciduous teeth in 3- 5-years-old children treated in Children's Department of Tehran University of Medical Sciences, Iran, using the spatial autologistic regression. The other objective was identifying a risk pattern of decayed dents of these children. MATERIALS AND METHODS: The study group consisted of 400 children (3- 5-years-old). Two groups of postgraduate and under graduate dentistry students under consideration and approval of the professors of dentistry from the Tehran University of Medical Sciences diagnosed and categorised the caries statuses of deciduous dents of the children. The caries statuses were considered as spatially correlated binary data. The appropriate model was autologistic regression. RESULTS: The fitted autologistic model showed that caries in the three nearest neighbours of a tooth, which includes the two adjacent and the one vertically opponent teeth, had significant effect on its caries. The computed risks based on the fitted model revealed a definite-spatial pattern of caries events. CONCLUSIONS: Every decayed deciduous tooth in the mouth of a preschool child threatens the three nearest teeth. The risk pattern of caries in each quarter of the teeth lattice of children from incisors to molars has an ascending rate. The dents in maxilla and posterior locations have higher risks of caries than in mandible and anterior locations. These findings are valuable in preventive health care and therapeutic approaches in dentistry of children.
