Incorporation of Alternative Ultrasound Biomarkers into Myometrial Invasion-based Model better Predicts Lymph Node Metastasis in Endometrial Cancer: Evidence and Future Prospects

Background: Myometrial invasion (MI) is a parameter currently used in transvaginal ultrasound (TVS) in endometrial cancer (EC) to determine local staging, however, without molecular diagnostics, it is insufficient for selection of high-risk cases, i.e., those with a high risk of lymph node metastases (LNM). Methods: One hundred sixteen consecutive EC patients, who had received 2D transvaginal ultrasound examinations in their preoperative workup and final histopathology results as a reference standard, were included in this prospective study. Univariate and multivariate logistic models of analyzed TVS biomarkers (tumor [T] size, T area [AREA], T volume [SPE-VOL], MI, T-free distance to serosa [TFD], endo-myometrial irregularity, [EMIR], cervical stromal involvement, CSI) were evaluated to assess the relative accuracy of the possible LNM predictors. To avoid a potential bias in assuming linear relations between LNM and continuous predictors, spline functions were applied. Calculations were made in R with the use of libraries splines, glmulti, and pROC. Results: LNM was found in 20 out of 116 (17%) patients. In univariate analysis, only uMI, EMIR, uCSI and uTFD were significant predictors of LNM. Accuracy was 0.707 (AUC 0.684, 95% CI 0.568-0.801) for uMI (p<0.01), 0.672 (AUC 0.664, 95% CI 0.547-0.781) for EMIR (p<0.01), 0.776 (AUC 0.647, 95% CI 0.529-0.765) for uCSI (p<0.01), and 0.638 (AUC 0.683, 95% CI 0.563-0.803) for uTFD (p<0.05). The cut-off value for uTFD was 5.2 mm. However, AREA and VOL revealed significant relation by non-linear analysis as well. Among all possible multivariate models, the one comprising interactions of splines of uTFD with uMI and splines of SPE-VOL with uCSI showed most usefulness. Accuracy was 0.802 (AUC 0.791, 95% CI 0.673-0.91) Conclusions: A combination of uTFD for patients with uMI>50%, and SPE-VOL for patients with uCSI, allows for the most accurate prediction of LNM in EC, rather than uMI alone.