Despite recently published randomized trials suggesting no survival benefit for routine lymphadenectomy in endometrial cancer [1,2], full pelvic and paraaortic lymphadenectomy is still recommended by many gynecologic oncologic societies and guideline committees [3-5]. However, although there is ongoing controversy concerning the benefit of routine lymphadenectomy [6-8], the general consensus is that there is a certain subset of patients in which the omission of routine lymphadenectomy may be justified [9-11].

For several decades, researchers have proposed several models to predict patients at low-risk for nodal metastasis [12-15]. Most of these prediction models were designed using surgicopathological parameters, such as depth of myometrial invasion or pathological grade [13,14,16,17]. Therefore, it has been frequently challenged whether we can apply frozen section results in these models due to the inaccuracy of frozen section examination [18,19], and many have claimed that routine lymphadenectomy is unavoidable [20].

Although many researchers have suggested several methods to identify the low-risk group of nodal metastasis before lymphadenectomy [21-25], many gynecologic oncologists are still skeptical about these results [26]. Moreover, there has been no consensus about the desirable performance of a prediction method. The American Society of Breast Surgeons recommended a false-negative rate of 5% or less in order to abandon axillary dissection [27]. Then, how should an acceptable false-negative rate of lymph node metastasis be determined in endometrial cancer?

To answer these questions, we began a multi-institutional, retrospective study. If we are able to estimate the innate false-negative rate of the final pathology-based models, we may also use that as a tool for determining clinical usefulness of pre- or intra-operative prediction models which are in development.

The records of 1,298 patients who received surgical management for uterine cancer were reviewed (Fig. 1). Of the 1,298 patients, 58 patients were excluded because of a diagnosis of non-epithelial cancer including carcinosarcoma, double primary tumor, or other metastatic cancer. Furthermore, 293 patients who did not undergo lymph node dissection were excluded. The characteristics of the remaining 947 patients are summarized in Table 2. As expected, the distribution of stage, tumor grade, myometrial invasion, lymphovascular space invasion (LVSI), and extra-uterine involvement were significantly different between the lymphadenectomy versus non-lymphadenectomy groups, representing the tendency to avoid lymphadenectomy in cases with fewer risk factors.

The negative predictive values (NPVs) and negative LRs were not statistically different among the three models (Table 3). However, the proportion of patients classified as low-risk group was significantly different among the models. Model A, which included LVSI information, identified the largest number of patients as a low-risk group (56.4%) without hampering the negative predictive value. Model C identified the smallest low-risk group (30.5%), although its predictive performance was similar to that of other models. In addition, using Bayes' theorem, the negative PTP could be calculated at the 10% of assumed prevalence of lymph node metastasis (Table 3). All models indicated that false negative rate might be 2% when the prevalence of lymph node metastasis was 10%.

In the current study, we compared the predictive performance of various prediction models to identify a low-risk group in a large cohort of patients with endometrial cancer. Several clinical implications suggested by our data are as follows.

First, our study revealed that three models based on surgical pathology showed similar negative predictive powers. Our data suggest that the low-risk group can be identified with a false negativity rate of 2% by final pathologic data (Table 3), regardless of the choice of prediction model. Second, although the false negativity of these models was similar, the model from the Gynecologic Oncology Group (GOG) pilot study [12,13], which included LVSI as a predictor, was able to identify the largest number of patients (56%) as a low-risk group. The proportions of patients in the low-risk group identified using those two models (A and C) were significantly different.

In summary, even with final pathologic data, the currently available prediction identifying the low-risk group of lymph node metastasis in endometrial cancer has a false negative rate about 2% at 10% of the assumed prevalence. Therefore, future pre-/intra-operative prediction models may be regarded as clinically useful if the model shows a false negative rate less than 2%, when the prevalence of nodal metastasis was assumed as 10%.