Predictive factors of low-Risk gestational trophoblastic neoplasia relapse: a cohort study

• Grant number: 24/23154-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2025
• End date: April 30, 2026
• Field of knowledge: Health Sciences - Medicine - Maternal and Child Health
• Principal Investigator: Izildinha Maestá
• Grantee: Maria Victória Dian Mathias
• Host Institution: Faculdade de Medicina (FMB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil

Abstract

Introduction: Approximately 5% of patients with low-risk gestational trophoblastic neoplasia (GTN) experience relapse after the completion of chemotherapy and normalization of the human chorionic gonadotropin (hCG) biomarker. However, there is no consensus on additional consolidation chemotherapy cycles after normalization is achieved. Identifying clinical factors that predict relapse is important to reduce or customize the administration of consolidation chemotherapy only to those who genuinely require it. Objective: The purpose of this study is to identify the clinical factors associated to increased risk of relapse in low-risk GTN patients treated with singe-agent or sequential single-agent chemotherapy. Methods: This retrospective cohort study (1990-2022) will include low-risk GTN patients treated at the Botucatu Trophoblastic Disease Center of São Paulo State University, and Charing Cross Trophoblastic Disease Centre (UK). Predictive clinical factors for relapse will be identified based on the analysis of clinical characteristics of the patients and the disease, as well as treatment characteristics. Relapse will be defined as the presence of at least two sequential increasing hCG values for at least four weeks after hCG normalization, and in the absence of a new pregnancy. A Poisson linear regression model will be applied to examine the associations between predictive variables and GTN relapse. Variables showing a significant association with relapse (p ¿ 0.20) will then be further analyzed using a multiple regression model.