Predictive factors of relapse in low-risk gestational trophoblastic neoplasia: a multicenter cohort study

• Grant number: 24/02607-4
• Support Opportunities: Regular Research Grants
• Start date: September 01, 2024
• End date: August 31, 2026
• Field of knowledge: Health Sciences - Medicine - Maternal and Child Health
• Principal Investigator: Izildinha Maestá
• Grantee: Izildinha Maestá
• Host Institution: Faculdade de Medicina (FMB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil
• Associated researchers: Antônio Rodrigues Braga Neto ; Elza Maria Hartmann Uberti ; Michael j Seckl ; Ross Berkowitz ; Sue Yazaki Sun

Abstract

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 develop a predictive model to estimate the risk of relapse in low-risk GTN patients treated with single-agent or sequential single-agent chemotherapy.Methods: This multicenter retrospective non-simultaneous cohort study (1974-2022 and 1990-2022) will include low-risk GTN patients from six trophoblastic disease reference centers. 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. To construct the predictive model, the database will be randomly divided into two subsets: the development sample (or training, 50%) and the validation sample (or test, 50%). The validation of the predictive model will involve assessments of calibration and discrimination. (AU)