Objectives: To predict placental accreta spectrum (PAS) in patients with placenta previa (PP) evaluating clinical risk factors (CRF), ultrasound (US) and magnetic resonance imaging (MRI) findings. Methods: Seventy patients with PP were retrospectively selected. CRF were retrieved from medical records. US and MRI images were evaluated to detect imaging signs suggestive of PAS. Univariable analysis was performed to identify CRF, US and MRI signs associated with PAS considering histology as standard of reference. Receiver operating characteristic curve (ROC) analysis was performed, and the area under the curve (AUC) was calculated. Multivariable analysis was also performed. Results: At univariable analysis, the number of previous cesarean section, smoking, loss of the retroplacental clear space, myometrial thinning < 1 mm, placental lacunae, intraplacental dark bands (IDB), focal interruption of myometrial border (FIMB) and abnormal vascularity were statistically significant. The AUC in predicting PAS progressively increased using CRF, US and MRI signs (0.69, 0.79 and 0.94, respectively; p < 0.05); the accuracy of MRI alone was similar to that obtained combining CRF, US and MRI variables (AUC = 0.97) and was significantly higher (p < 0.05) than that combining CRF and US (AUC = 0.83). Multivariable analysis showed that only IDB (p = 0.012) and FIMB (p = 0.029) were independently associated with PAS. Conclusions: MRI is the best modality to predict PAS in patients with PP independently from CRF and/or US finding. It is reasonable to propose the combined assessment of CRF and US as the first diagnostic level to predict PAS, sparing MRI for selected cases in which US findings are uncertain for PAS.
Prediction of placenta accreta spectrum in patients with placenta previa using clinical risk factors, ultrasound and magnetic resonance imaging findings / Romeo, V.; Verde, F.; Sarno, L.; Migliorini, S.; Petretta, M.; Mainenti, P. P.; D'Armiento, M.; Guida, M.; Brunetti, A.; Maurea, S.. - In: LA RADIOLOGIA MEDICA. - ISSN 0033-8362. - 126:9(2021), pp. 1216-1225. [10.1007/s11547-021-01348-6]
Prediction of placenta accreta spectrum in patients with placenta previa using clinical risk factors, ultrasound and magnetic resonance imaging findings
Romeo V.;Verde F.;Sarno L.;Migliorini S.;Mainenti P. P.;D'Armiento M.;Guida M.;Brunetti A.;Maurea S.
2021
Abstract
Objectives: To predict placental accreta spectrum (PAS) in patients with placenta previa (PP) evaluating clinical risk factors (CRF), ultrasound (US) and magnetic resonance imaging (MRI) findings. Methods: Seventy patients with PP were retrospectively selected. CRF were retrieved from medical records. US and MRI images were evaluated to detect imaging signs suggestive of PAS. Univariable analysis was performed to identify CRF, US and MRI signs associated with PAS considering histology as standard of reference. Receiver operating characteristic curve (ROC) analysis was performed, and the area under the curve (AUC) was calculated. Multivariable analysis was also performed. Results: At univariable analysis, the number of previous cesarean section, smoking, loss of the retroplacental clear space, myometrial thinning < 1 mm, placental lacunae, intraplacental dark bands (IDB), focal interruption of myometrial border (FIMB) and abnormal vascularity were statistically significant. The AUC in predicting PAS progressively increased using CRF, US and MRI signs (0.69, 0.79 and 0.94, respectively; p < 0.05); the accuracy of MRI alone was similar to that obtained combining CRF, US and MRI variables (AUC = 0.97) and was significantly higher (p < 0.05) than that combining CRF and US (AUC = 0.83). Multivariable analysis showed that only IDB (p = 0.012) and FIMB (p = 0.029) were independently associated with PAS. Conclusions: MRI is the best modality to predict PAS in patients with PP independently from CRF and/or US finding. It is reasonable to propose the combined assessment of CRF and US as the first diagnostic level to predict PAS, sparing MRI for selected cases in which US findings are uncertain for PAS.File | Dimensione | Formato | |
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