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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/186850
Mid-trimester prediction of spontaneous preterm birth with automated cervical quantitative ultrasound texture analysis and cervical length: a prospective study
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The objective of this study was to evaluate a novel automated test based on ultrasound cervical texture analysis to predict spontaneous Preterm Birth (sPTB) alone and in combination with Cervical Length (CL). General population singleton pregnancies between 18+ 0 and 24 + 6 weeks' gestation were assessed prospectively at two centers. Cervical ultrasound images were evaluated and the occurrence of sPTB before weeks 37 + 0 and 34 + 0 were recorded. CL was measured on-site. The automated texture analysis test was applied ofine to all images. Their performance to predict the occurrence of sPTB before 37 + 0 and 34 + 0 weeks was evaluated separately and in combination on 633 recruited patients. AUC for sPTB prediction before weeks 37 and 34 respectively were as follows: 55.5% and 65.3% for CL, 63.4% and 66.3% for texture analysis, 67.5% and 76.7% when combined. The new test improved detection rates of CL at similar low FPR. Combining the two increased detection rate compared to CL alone from 13.0 to 30.4% for sPTB< 37 and from 14.3 to 42.9% sPTB< 34. Texture analysis of cervical ultrasound improved sPTB detection rate compared to cervical length for similar FPR, and the two combined together increased signifcantly prediction performance. This results should be confrmed in larger cohorts.
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BURGOS ARTIZZU, Xavier p., BAÑOS, Núria, CORONADO GUTIÉRREZ, David, PONCE, Júlia, Valenzuela-Alcaraz B, MORENO ESPINOSA, Ana l., GRAU, Laia, PÉREZ MORENO, Álvaro, GRATACÓS SOLSONA, Eduard, PALACIO, Montse. Mid-trimester prediction of spontaneous preterm birth with automated cervical quantitative ultrasound texture analysis and cervical length: a prospective study. _Scientific Reports_. 2021. Vol. 11, núm. 2021, pàgs. 7469. [consulta: 21 de gener de 2026]. ISSN: 2045-2322. [Disponible a: https://hdl.handle.net/2445/186850]