ArticleInternational journal of molecular sciences2025
Hydatidiform Moles: The Contribution of Ancillary Techniques in Refining Their Histopathological Diagnosis.
Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Patch-to-slide fusion deep learning model for histological diagnosis of early pregnancy loss including hydatidiform mole.NPJ digital medicine · 2026Article
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5 authors.
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Abstract
A hydatidiform mole (HM) is the most common form of gestational trophoblastic disease (GTD). Differentiating hydatidiform moles (HMs) from non-molar pregnancies and distinguishing complete HMs (CHMs) from partial HMs (PHMs) remains challenging due to overlapping morphological features and a high rate of misclassification. This study aimed to evaluate reliable immunohistochemical markers for improving diagnostic accuracy and addressing the limitations of current molecular techniques. We retrospectively analyzed 64 cases of HMs and hydropic abortions (HAs), diagnosed in women aged 17-36 years between 2010 and 2024, at the Pathology Department of "Elena Doamna" Clinical Hospital, Iași, Romania. Routine histology was supplemented with immunohistochemistry (IHC) using p57, Ki-67, β-hCG, and E-cadherin, with semiquantitative immunoscores applied. Histology revealed 38 PHMs (59.37%), 16 CHMs (23.88%), and 10 HAs (15.62%). p57 was positive in 100% of PHMs and HAs but only in 18% of CHMs. Ki-67 expression was predominantly strong in CHMs, variable in PHMs, and weak in all HAs. β-hCG showed the highest expression in CHMs, followed by PHMs and HAs, while E-cadherin was strongest in HAs. Morphological features alone are insufficient for HM diagnosis; thus, ancillary techniques like p57 IHC and DNA genotyping are crucial to differentiate complete, partial moles, and non-molar specimens by revealing unique genetic patterns, especially p57 absence in CHMs and ploidy/parental origin in PHMs. In this context, an algorithmic approach integrating histology, immunohistochemistry, and genotyping reduces interobserver variability and refines diagnostic precision.
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