Risk Stratification of Postoperative Adjuvant Therapy for Endometrial Cancer (POAT-ENDORISK) Based on Bayesian Network Model: A Development and Validation Study.

PURPOSE/OBJECTIVE(S): To establish a Bayesian network (BN) model for postoperative adjuvant treatment of early endometrial carcinoma (EC) patients.

MATERIALS/METHODS: We retrospectively analyzed the data of 1280 early EC patients treated by multiple institutions in China from 1999 to 2017. All patients received primary hysterectomy/bilateral salpingo-oophorectomy and adjuvant radiotherapy. The FIGO 2009 stage of all patients is stage I and stage II EC, and the median age is 57 years old. All patients are grouped according to the ESMO-ESGO-ESTRO risk stratification. The clinicopathologic factors, treatment-related factors, local regional recurrence, distant metastasis and cancer-specific survival rate (CSS) of all patients were reviewed. We divide the original data set into training set and Validation set according to the ratio of 7:3. The training of the Bayesian network model is completed on Netica, and the test of the model effect is finally completed on the test set.

RESULTS: After variable screening, a total of 14 characteristic variables entered the final model. A total of 896 patients were used for the development of BN model, and 384 patients were used for the validation of BN model. The results of the model showed that the factors directly related to CSS were locoregional failure (LRF), radiotherapy mode, distant metastasis (DM). Factors directly related to DM were chemotherapy, LRF, CSS. The factors directly related to LRF were risk stratification, preoperative serum CA125 and preoperative HB. The accuracy, sensibility, specificity, micro-f1, micro-f1, weighted-f1 and AUC of BN model in predicting DM and CSS were better than XGBoost model.

CONCLUSION: In this study, we integrated almost all clinical pathology and treatment information related to postoperative adjuvant treatment of early EC patients and established a BN model for personalized clinical decision-making of postoperative adjuvant treatment of early EC patients. The results showed the complex correlation among the variables, and the overall prediction ability and visualization effect of BN model was significantly better than XGBoost model. Prospective research is needed before clinical implementation.