A clinical marker-based modeling framework to preoperatively predict lymph node and vascular space involvement in endometrial cancer patients

Introduction: Endometrial cancer (EC) has high mortality at advanced stages. Poor prognostic factors include grade 3 tumors, deep myometrial invasion, lymph node metastasis (LNM), and lymphovascular space invasion (LVSI). Preoperative knowledge of patients at higher risk of lymph node involvement, when such involvement is not suspected, would benefit surgery planning and patient prognosis. This study implements an ensemble machine learning approach that evaluates Cancer Antigen 125 (CA125) along with histologic type, preoperative grade, and age to predict LVSI, LNM and stage in EC patients.

Methods: A retrospective chart review spanning January 2000 to January 2015 at a regional hospital was performed. Women 18 years or older with a diagnosis of EC and preoperative or within one-week CA125 measurement were included (n = 842). An ensemble machine learning approach was implemented based on a stacked generalization technique to evaluate CA125 in combination with histologic type, preoperative grade, and age as predictors, and LVSI, LNM and disease stage as outcomes.

Results: The ensemble approach predicted LNM and LVSI in EC patients with AUROC_TEST of 0.857 and 0.750, respectively, and predicted disease stage with AUROC_TEST of 0.665. The approach achieved AUROC_TEST for LVSI and LNM of 0.750 and 0.643 for grade 1 patients, and of 0.689 and 0.952 for grade 2 patients, respectively.

Conclusion: An ensemble machine learning approach offers the potential to preoperatively predict LVSI, LNM and stage in EC patients with adequate accuracy based on CA125, histologic type, preoperative grade, and age.