Demographics, risk factors associated with malignant progression, and pregnancy outcomes of hydatidiform mole: A retrospective cohort study in Shanghai, China - Zhu, Tingting; Zhu, Guohua; Ma, Jiahui; Yu, Xiaojuan; Chen, Tingting; Tao, Xiang; Sun, Li; Lu, Xin; Du, Yan (Frontiers in Oncology, 2025)
ObjectiveThis study aimed to clarify the demographics, evaluate risk factors associated with malignant progression, and assess pregnancy outcomes among patients with hydatidiform mole (HM) using a large, retrospective cohort study in Shanghai.MethodsThis retrospective cohort study included patients with pathologically confirmed HM from 2019 to 2023. Descriptive analyses were performed to describe the demographic characteristics, progression to postmolar gestational trophoblastic neoplasia (pGTN), and reproductive outcomes of patients. Univariate and multivariate logistic regression analyses were conducted to evaluate risk factors and develop predictive models for pGTN.ResultsOf 506 patients with HM, the average age and gestational age at diagnosis were approximately 33 years and 10 weeks, respectively. During follow-up, 42 patients (8.3%) progressed to pGTN, all achieved complete response after treatment. Univariate and multivariate analyses revealed that significant risk factors for progression to pGTN included pathological type and maximum diameter of lesions by ultrasound pre-evacuation (p < 0.05 for both). A predictive model incorporating age, β-hCG ratio (before/after evacuation), and ultrasound characteristics (uterine/lesion ratio) demonstrated optimal performance and goodness of fit. Among the 304 patients who intended to conceive, 254 had documented reproductive outcomes (follow-up rate: 83.6%). Of these, 163 patients (64.2%) achieved successful re-pregnancy, including 131 (80.4%) livebirths.ConclusionOur study provides a comprehensive update on the demographics, risk factors associated with progression to pGTN, and reproductive outcomes of patients with HM in Shanghai. The clinical application of the predictive model needs to be further verified in a longitudinal setting.