Establishment of a Bayesian network model to predict the survival of malignant peritoneal mesothelioma patients after cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy.

Objectives: To establish a Bayesian network (BN) model to predict the survival of patients with malignant peritoneal mesothelioma (MPM) treated with cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC).

Methods: The clinicopathological data of 154 MPM patients treated with CRS + HIPEC at our hospital from April 2015 to November 2022 were retrospectively analyzed. They were randomly divided into two groups in a 7:3 ratio. Survival analysis was conducted on the training set and a BN model was established. The accuracy of the model was validated using a confusion matrix of the testing set. The receiver operating characteristic (ROC) curve and area under the curve were used to evaluate the overall performance of the BN model.

Results: Survival analysis of 107 patients (69.5%) in the training set found ten factors affecting patient prognosis: age, Karnofsky performance score, surgical history, ascites volume, peritoneal cancer index, organ resections, red blood cell transfusion, pathological types, lymphatic metastasis, and Ki-67 index (all  < 0.05). The BN model was successfully established after the above factors were included, and the BN model structure was adjusted according to previous research and clinical experience. The results of confusion matrix obtained by internal validation of 47 cases in the testing set showed that the accuracy of BN model was 72.7%, and the area under ROC was 0.74.

Conclusions: The BN model was established successfully with good overall performance and can be used as a clinical decision reference.