AUTHOR=Li Ruikai , Zhang Chi , Du Kunli , Dan Hanjun , Ding Ruxin , Cai Zhiqiang , Duan Lili , Xie Zhenyu , Zheng Gaozan , Wu Hongze , Ren Guangming , Dou Xinyu , Feng Fan , Zheng Jianyong 

TITLE=Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.842970

DOI=10.3389/fpubh.2022.842970

ISSN=2296-2565

ABSTRACT=Background: The existing prognostic models of rectal cancer after radical resection ignored the relationships among prognostic factors and their mutual effects on prognosis. Thus, a new modeling method is required to remedy this defect. The aim of the present study was to construct a new prognostic prediction model based on Bayesian network (BN), a machine learning tool for data mining and for clinical decision making and prognostic prediction.
Methods: From January 2015 to December 2017, the clinical data of 705 rectal cancer patients underwent radical resection was analyzed. The entire cohort was divided into training and testing dataset. A new prognostic prediction model based on BN was constructed and compared with nomogram.
Results: Univariate analysis showed that age, CEA, CA19-9, CA125, preoperative chemotherapy, macropathology type, tumor size, differentiation status, T stage, N stage, vascular invasion, KRAS mutation and postoperative chemotherapy were associated with overall survival (OS) of the training dataset. Based on the above-mentioned variables, a 3-year OS prognostic prediction BN model of the training dataset was constructed using Tree Augmented Naïve Bayes method. In addition, age, CEA, CA19-9, CA125, differentiation status, T stage, N stage, KRAS mutation and postoperative chemotherapy were identified as independent prognostic factors of the training dataset through multivariate Cox regression and were used to construct a nomogram. Then, based on the testing dataset, the two models were evaluated using receiver operating characteristic (ROC) curve. The results showed that the area under the curve of ROC of BN model and nomogram was 80.11% and 74.23%, respectively.
Conclusion: The present study established a BN model for prognostic prediction of rectal cancer for the first time, and which was demonstrated to be more accurate than nomogram.