Estimation Recurrence Free Survival of the Epithelial Ovarian Cancer Using Classification and Regression Tree

Abstract

Background: Epithelial ovarian cancer is one of the leading causes of death from gynecological cancers in the Western world. One of the important objectives of medical research is to determine predictors of an event. Regarding the interaction of risk factors, regression methods are unsuitable when the number of factors is high. Objectives: Regarding frequency predictors of recurrence-free survival in epithelial ovarian cancer, our aim in this article is to determine predictors and time to first recurrence using a classification and regression tree model. Methods: This retrospective analysis used medical and chemotherapy records of 141 patients with epithelial ovarian cancer between 2007 and 2018. They were referred to Imam Hossein Hospital in Tehran. Data were analyzed using classification and regression trees in Rver3.4.3. Results: The regression tree results showed that the worst recurrence-free survival in metastatic patients was in grade II patients (15.03 ± 11 months), but in patients without metastases were in patients with CA125 tumor marker above 207 that used 3-week chemotherapy courses (14.53 ± 6.4 months). The classification tree also showed that the most probability of the first recurrence in metastatic patients was in patients with adjuvant chemotherapy (0.81), and patients without metastases were among those with stages 2, 3, and 4 with the maximum platelet count above 305,000 and less than 35 years old (0.75). Conclusions: The classification and regression tree models, without any assumptions, can estimate the probability of recurrence in different subgroups. These models can be used in deciding due to the ease of interpretation by physicians and paramedics.