Survival prediction by Bayesian network modeling for pseudomyxoma peritonei after cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy. (21st August 2022)

• Record Type: Journal Article
• Authors: Zhao, Xin; Li, Xinbao; Lin, Yulin; Ma, Ru; Zhang, Ying; Xu, Dazhao; Li, Yan
• Is Part Of: Cancer medicine. Volume 12:Number 3(2023)
• Page Start: 2637
• Page End: 2645
• Publication Date: 2022-08-21
• Subjects: Bayesian network -- cytoreductive surgery -- hyperthermic intraperitoneal chemotherapy -- pseudomyxoma peritonei -- survival prognostic model; 616.994005
• DOI: 10.1002/cam4.5138
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract: Objectives: To establish a survival prognostic model for pseudomyxoma peritonei (PMP) treated with cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC) based on Bayesian network (BN). Methods: 453 PMP patients were included from the database at our center. The dataset was divided into a training set to establish BN model and a testing set to perform internal validation at a ratio of 8:2. From the training set, univariate and multivariate analyses were performed to identify independent prognostic factors for BN model construction. The confusion matrix, receiver operating characteristic (ROC) curve and the area under curve (AUC) were used to evaluate the performance of the BN model. Results: The univariate and multivariate analyses identified 7 independent prognostic factors: gender, previous operation history, histological grading, lymphatic metastasis, peritoneal cancer index, completeness of cytoreduction and splenectomy (all p < 0.05). Based on independent factors, the BN model of training set was established. After internal validation, the accuracy and AUC of the BN model were 70.3% and 73.5%, respectively. Conclusion: The BN model provides a reasonable level of predictive performance for PMP patients undergoing CRS + HIPEC. Abstract : This study developed a Bayesian network (BN) survival prognostic model for PMP which has a resonable level of predictive performation. The BN model can help oncologists to make clinical decisions.