Combining Clinicopathological Parameters and Molecular Indicators to Predict Lymph Node Metastasis in Endometrioid Type Endometrial Adenocarcinoma - Jiang, Peng; Huang, Yuzhen; Tu, Yuan; Li, Ning; Kong, Wei; Di, Feiyao; Jiang, Shan; Zhang, Jingni; Yi, Qianlin; Yuan, Rui (Frontiers in Oncology, 2021)
Background: Lymph node metastasis (LNM) is an important unfavorable prognostic factor for endometrial cancer (EC). At present, models involving molecular indicators to accurately predict LNM are still very rare. We addressed this gap by developing nomograms to individualize the risk of LNM in EC and to identify a low-risk group of LNM. Method: 776 patients who underwent comprehensive surgical staging with pelvic lymphadenectomy at the First Affiliated Hospital of Chongqing Medical University were divided into training cohort (used for building model) and validation cohort (used for validating model) according to a predefined ratio of 7:3. Logistics regression analysis was used in training cohort to screen out predictors related to LNM and then a nomogram was developed to predict LNM of patients. Calibration curve and consistency index (C-index) was used to estimate the performance of the model. Receiver Operating Characteristic (ROC) curve and Youden index were used to determine the optimal threshold of the risk probability of LNM predicted by the model proposed in this study, and then the prediction performance of different models and their discrimination for identifying low-risk patients were compared. Result: LNM respectively occurred in 87 and 42 patients in training cohort and validation cohort. Multivariate logistic regression analysis showed histological grade(P=0.022), myometrial invasion(P=0.002), lymphatic vessel space invasion (LVSI)(P=0.001), serum Ca125(P=0.008), Ki67(P=0.012), estrogen receptor (ER) (0.009) and P53 (P=0.003) were associated with LNM and then a nomogram was successfully established on this basis. The internal and external calibration curves showed that the model fitted well, and C-index showed the prediction accuracy of the model proposed in this study was better than other models (the C-index of training cohort and validation cohort were 0.90 and 0.91, respectively). The optimal threshold of the risk probability of LNM predicted by the model was 0.18. On the basis of this threshold, the model showed a good discrimination of identifying low-risk patients. Conclusion: Combining molecular indicators on the basis of classic clinical parameters can more accurately predict the LNM of patients with EC, and the nomogram proposed in this study showed a good discrimination of identifying low-risk patients of LNM.