Defining the optimal lymphadenectomy cut-off value in epithelial ovarian cancer staging surgery utilizing a mathematical model of validation

Abstract

Objective

Since 1985 International Federation of Gynecology and Obstetrics includes pelvic and aortic lymphadenectomy as part of the surgical staging in epithelial ovarian cancer (EOC). There is no consensus on the overall number of nodes needed in a systematic lymphadenectomy. The aim of this study is to calculate the optimal cut-off value using a mathematical modeling approach.

Methods

Data was collected retrospectively, from 1996 to 2000, of 120 consecutive Mayo Clinic patients with EOC and positive nodes. All patients was underwent pelvic and/or aortic lymphadnectomy during surgical staging.

To mathematically predict the probability of a positive node in EOC patients we used a predictive mathematical model (PMM). The mathematical analysis consisted: creation of a new PMM according to our purposes, application of PMM to describe the experimental data in order to build the polynomial regression curves in each lymphatic area and determine the optimal point for each curve.

Results

The mean number of lymph nodes and metastatic nodes removed were 35 and 7.8, respectively; the mean percentage of positive nodes was 28.3%. The optimal point of each fitting curves were: 7 nodes for unilateral aortic nodal sampling (at least 3 infrarenal or 5 inframesenteric) and 15 nodes for unilateral pelvic lymphadenectomy (at least 5 external iliac).

Conclusions

We can mathematically predict the probability to obtain a positive node in EOC surgical staging. Our results have shown the need to obtain at least 22 lymph nodes between pelvic and aortic lymphadenectomy.

Introduction

Probability provides a quantitative description of the likelihood of an occurrence of a particular event if this event is repeated several times, under similar conditions. The calculation of probability thereby offers a tool to quantify outcome parameters of the problem, and is the basis of inferential or inductive statistics.¹ Therefore, we can theoretically and mathematically predict the probability of positive lymph nodes in patients with epithelial ovarian carcinoma (EOC) through a predictive mathematical model (PMM) based on binomial laws of distribution.²

EOC, by 1985 International Federation of Gynecology and Obstetrics is a surgically staged disease. This staging system includes pelvic and aortic lymphadenectomy as part of the procedure.³ There is no consensus in the literature however on the definition of systematic lymphadenectomy (SL).⁴ Although the prognostic significance of pelvic and aortic SL in primary staging laparotomy is well established,⁵ the role of SL in improving overall survival still remains controversial.⁶ The prediction of outcomes of a pelvic or aortic lymphadenectomy is important in the management of EOC surgical especially when performing a radical nodal lymphadenectomy in patients with a high probability to obtain a negative nodal disease. Such procedures may increase patient morbidity without benefit.⁷ Therefore, it would be very beneficial to know a potential “cut-off” point in pelvic and aortic lymphadenectomy for which the surgeon knows when to stop or continue the sampling procedure. Currently, many author's believe that it is beneficial for EOC patients with advanced disease to undergo an extensive removal of retroperitoneal disease to improve survival,4, 6, 8, 9, 10, 11 promote the response to chemotherapy,¹² decrease retroperitoneal recurrences¹³ and as a means of ensuring complete cytoreduction to microscopic disease status.⁶

The goal of this study is to the optimal role of lymphadenectomy in patients with EOC, using mathematical modeling. This approach permits us to calculate the optimal values of the nodes number to be removed during the lymphadenectomy surgical staging.