Using a new diagnostic tool to predict lymph node metastasis in advanced epithelial ovarian cancer leads to simple...

The aim of this study was to develop a new diagnostic tool to predict lymph node metastasis (LNM) in patients with advanced epithelial ovarian cancer undergoing primary cytoreductive surgery.

The FRANCOGYN group's multicenter retrospective ovarian cancer cohort furnished the patient population on which we developed a logistic regression model. The prediction model equation enabled us to create LNM risk groups with simple lymphadenectomy decision rules associated with a user-friendly free interactive web application called shinyLNM.

277 patients from the FRANCOGYN cohort were included; 115 with no LNM and 162 with LNM. Three variables were independently and significantly (p<0.05) associated with LNM in multivariate analysis: pelvic and/or para-aortic LNM on CT and/or PET/CT (p<0.00), initial PCI ≥ 10 and/or diaphragmatic carcinosis (p = 0.02), and initial CA125 ≥ 500 (p = 0.02). The ROC-AUC of this prediction model after leave-one-out cross-validation was 0.72. There was no difference between the predicted and the observed probabilities of LNM (p = 0.09). Specificity for the group at high risk of LNM was 83.5%, the LR+ was 2.73, and the observed probability of LNM was 79.3%; sensitivity for the group at low-risk of LNM was 92.0%, the LR- was 0.24, and the observed probability of LNM was 25.0%.

This new tool may prove useful for improving surgical planning and provide useful information for patients.