Combining Clinicopathological Parameters and Molecular Indicators to Predict Lymph Node Metastasis in Endometrioid Type Endometrial Adenocarcinoma

Abstract

BackgroundLymph node metastasis (LNM) is a critical unfavorable prognostic factor in endometrial cancer (EC). At present, models involving molecular indicators that accurately predict LNM are still uncommon. We addressed this gap by developing nomograms to individualize the risk of LNM in EC and to identify a low-risk group for LNM.MethodsIn all, 776 patients who underwent comprehensive surgical staging with pelvic lymphadenectomy at the First Affiliated Hospital of Chongqing Medical University were divided into a training cohort (used for building the model) and a validation cohort (used for validating the model) according to a predefined ratio of 7:3. Logistics regression analysis was used in the training cohort to screen out predictors related to LNM, after which a nomogram was developed to predict LNM in patients with EC. A calibration curve and consistency index (C-index) were used to estimate the performance of the model. A 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. Then, the prediction performance of different models and their discrimination abilities for identifying low-risk patients were compared.ResultLNM occurred in 87 and 42 patients in the training and validation cohorts, respectively. Multivariate logistic regression analysis showed that histological grade (P=0.022), myometrial invasion (P=0.002), lymphovascular 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; a nomogram was then successfully established on this basis. The internal and external calibration curves showed that the model fits well, and the C-index showed that the prediction accuracy of the model proposed in this study was better than that of the other models (the C-index of the training and validation cohorts was 0.90 and 0.91, respectively). The optimal threshold of the risk probability of LNM predicted by the model was 0.18. Based on this threshold, the model showed good discrimination for identifying low-risk patients.ConclusionCombining molecular indicators based on classical clinical parameters can predict LNM of patients with EC more accurately. The nomogram proposed in this study showed good discrimination for identifying low-risk patients with LNM.

Systemic Immune-Inflammatory Index as a Predictor of Lymph Node Metastasis in Endometrial Cancer

“…In addition, CA125 and CA153 were confirmed to be independent risk factors for LNM in EC patients in our study, which was consistent with previous studies. [31][32][33] Moreover, our study combined the SII, CA125, CA153 and LVSI to significantly improve the efficacy of predicting LNM in EC patients.…”

External validation study of endometrial cancer preoperative risk stratification model (ENDORISK)

“…Previous models used traditional clinicopathological characteristics including LVSI, myometrial invasion, histotype, grade, age, and/or BMI (11,12). So far, results were only moderate and, currently, additional immunohistochemical markers are already frequently used in the clinic: ER, PR, L1CAM, p53, Ki67 (13,14). Some authors also included imaging information such as tumor diameter, myometrial/cervical invasion, or lymphadenopathy (15,16).…”

A Model to Identify Candidates for Lymph Node Dissection Among Patients With High-Risk Endometrial Endometrioid Carcinoma According to Mayo Criteria

“…The predictive accuracy according to the discrimination of the AUC criteria was 0.64 for the nomogram. Recently, several studies have managed to combine clinicopathological parameters and molecular indicators to predict lymph node metastasis in EEC ( 20 , 26 – 29 ). It was showed that incorporating molecular indicators can predict lymph node metastasis more accurately ( 20 ).…”

“…Recently, several studies have managed to combine clinicopathological parameters and molecular indicators to predict lymph node metastasis in EEC ( 20 , 26 – 29 ). It was showed that incorporating molecular indicators can predict lymph node metastasis more accurately ( 20 ). However, POLE and MMR are important parameters in the molecular-based classification introduced by TCGA.…”