ArticleFrontiers in oncology2025
Demographics, risk factors associated with malignant progression, and pregnancy outcomes of hydatidiform mole: A retrospective cohort study in Shanghai, China.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Review
- Insights into Reproductive Immunology and Placental Pathology, 2nd Edition.International journal of molecular sciences · 2026Article
- Hydatidiform Moles: The Contribution of Ancillary Techniques in Refining Their Histopathological Diagnosis.International journal of molecular sciences · 2025Article
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Authors and funding
9 authors.
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Abstract
Objective: This 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. Methods: This 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. Results: Of 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 ( Conclusion: Our 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.
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