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.
Highlights
Endometrial cancer (EC) is a common gynecological malignant tumor with a high overall survival rate
Systematic pelvic and para-aortic lymph node dissection was recommended for patients with high-risk factors, including grade 3 type 1 EC, deep myometrial invasion, and pelvic sentinel lymph node metastasis seen on intraoperative histological examination or final histological examination [3]
According to the criteria proposed by AlHilli et al, removal of at least 10 pelvic LNs with or without five para-aortic LNs was defined as effective lymph node dissection [13]
Summary
Endometrial cancer (EC) is a common gynecological malignant tumor with a high overall survival rate. For some patients with early-stage EC, the incidence of complications, including lymphatic cysts, deep vein thrombosis, and intestinal obstruction, increases [5], increasing hospitalization expenses and the need for medical resources [6] This has led to the most current international guidelines to no longer recommend systematic lymph node dissection for patients with type I (endometrioid histologic type) EC. Sofiane et al [3] established a nomogram involving four clinicopathological parameters (histological grade, lymphovascular space invasion, TD, and MI) to predict LNM in EC It seems that these risk stratification systems and prediction models can no longer accurately predict LNM [9].
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