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.
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.