Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer

Abstract

Abstract Lymph node metastasis (LNM) is a well-established prognostic factor in endometrial cancer (EC). We aimed to construct a model that predicts LNM and prognosis using preoperative factors such as myometrial invasion (MI), enlarged lymph nodes (LNs), histological grade determined by endometrial biopsy, and serum cancer antigen 125 (CA125) level using two independent cohorts consisting of 254 EC patients. The area under the receiver operating characteristic curve (AUC) of the constructed model was 0.80 regardless of the machine learning techniques. Enlarged LNs and higher serum CA125 levels were more significant in patients with low-grade EC (LGEC) and LNM than in patients without LNM, whereas deep MI and higher CA125 levels were more significant in patients with high-grade EC (HGEC) and LNM than in patients without LNM. The predictive performance of LNM in the HGEC group was higher than that in the LGEC group (AUC = 0.84 and 0.75, respectively). Patients in the group without postoperative pathological LNM and positive LNM prediction had significantly worse relapse-free and overall survival than patients with negative LNM prediction (log-rank test, P < 0.01). This study showed that preoperative clinicopathological factors can predict LNM with high precision and detect patients with poor prognoses. Furthermore, clinicopathological factors associated with LNM were different between HGEC and LGEC patients.