Evaluation of a method of predicting lymph node metastasis in endometrial cancer based on five pre-operative characteristics

Abstract

Objective: We recently developed an algorithm based on five clinical and pathological characteristics to predict lymph node (LN) metastasis in endometrial cancer. The aim of this study was to evaluate the accuracy of using this algorithm with preoperative characteristics.

Study design: In this retrospective multicenter study, we evaluated the accuracy of using an algorithm to predict LN metastasis using preoperative tumor characteristics provided by endometrial sampling pathological characteristics (histological subtype and grade) and by magnetic resonance imaging (MRI) for primary site tumor extension.

Results: In total, 181 patients were included in this study, and 14 patients had pelvic LN metastasis (7.7%). Using preoperative tumor characteristics, the algorithm showed good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.83 (95% confidence interval (IC95)=0.79-0.87) and was well calibrated (average error=1.9% and maximal error=8.5%). LN metastasis prediction by the algorithm using preoperative data was as accurate as that obtained using the final tumor characteristics (AUC=0.77 (CI95=0.70-0.83), average error=2.8% and maximal error=23.2%).

Conclusion: Our algorithm was accurate in predicting pelvic LN metastasis even with the use of preoperative tumor characteristics provided by endometrial sampling and MRI. These findings, however, should be verified in a larger database before our algorithm is implemented for widespread use.

Keywords: Algorithm; Endometrial cancer; Lymph node metastasis; Preoperative data.