Table · dataset · 2026
Table 1_Diagnostic performance of standardized prenatal ultrasound signs for predicting Hecht histopathological grade in placenta accreta spectrum: a retrospective study.docx
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Description
Objective<p>To evaluate the diagnostic performance of prenatal ultrasound in predicting histopathological grade of placenta accreta spectrum (PAS) and to assess associated maternal and neonatal outcomes.</p>Methods<p>We performed a retrospective cohort study based on prospectively collected standardized prenatal ultrasound data, including patients with suspected PAS referred to a tertiary center between 2018 and 2025.
Eligible patients underwent standardized prenatal ultrasound assessment and delivery at our institution with histopathological evaluation. Ultrasound signs included myometrial thinning, loss of the clear zone, placental lacunae, utero-vesical hypervascularity (bridging vessels), abnormal placental–uterine interface, and placental bulging. Patients were categorized into low-grade (Hecht grade ≤2 or BPMF) and high-grade (Hecht grade ≥3A) groups.
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Diagnostic accuracy measures were calculated for each sign.</p>Results<p>Forty patients met inclusion criteria (15 low-grade, 25 high-grade). Diagnostic performance was assessed in the 35 women with an assignable Hecht grade. Myometrial thinning and placental lacunae demonstrated the highest sensitivity for predicting high-grade PAS (92.0% each), while bridging vessels and placental bulging showed the highest specificity among the signs (70.0% each).
High-grade cases were associated with greater estimated blood loss, lower gestational age at delivery, and lower birth weight. Hysterectomy and internal iliac artery ligation were more frequent in the high-grade group.</p>Conclusion<p>In this cohort, prenatal ultrasound assessment showed differential ability to discriminate high-grade PAS confirmed by histopathology. Higher histological grade was associated with increased maternal morbidity.</p>
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- DOI doi.org/10.3389/fmed.2026.1933234.s001 ↗
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- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
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Topics
- From keywords
- Astronomy & Astrophysics · Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Foetal development and medicine · Humanities · Life Sciences · Medicine & Health · Ocean & Atmospheric Science · Social Science
- Inferred from text
- Longitudinal study 65% · Tabular 65%
Provenance · 1 source records, 19 field assertions
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