Department of Radiology, The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China.
Department of Medical Imaging, Yueyang Central Hospital, Yueyang, Hunan, China.
Eur Radiol. 2024 Feb;34(2):957-969. doi: 10.1007/s00330-023-10058-8. Epub 2023 Aug 17.
To develop and validate MRI-based scoring models for predicting placenta accreta spectrum (PAS) invasiveness.
This retrospective study comprised a derivation cohort and a validation cohort. The derivation cohort came from a systematic review of published studies evaluating the diagnostic performance of MRI signs for PAS and/or placenta percreta in high-risk women. The significant signs were identified and used to develop prediction models for PAS and placenta percreta. Between 2016 and 2021, consecutive high-risk pregnant women for PAS who underwent placental MRI constituted the validation cohort. Two radiologists independently evaluated the MRI signs. The reference standard was intraoperative and pathologic findings. The predictive ability of MRI-based models was evaluated using the area under the curve (AUC).
The derivation cohort included 26 studies involving 2568 women and the validation cohort consisted of 294 women with PAS diagnosed in 258 women (88%). Quantitative meta-analysis revealed that T2-dark bands, placental/uterine bulge, loss of T2 hypointense interface, bladder wall interruption, placental heterogeneity, and abnormal intraplacental vascularity were associated with both PAS and placenta percreta, and myometrial thinning and focal exophytic mass were exclusively associated with PAS. The PAS model was validated with an AUC of 0.90 (95% CI: 0.86, 0.93) for predicting PAS and 0.85 (95% CI: 0.79, 0.90) for adverse peripartum outcome; the placenta percreta model showed an AUC of 0.92 (95% CI: 0.86, 0.98) for predicting placenta percreta.
MRI-based scoring models established based on quantitative meta-analysis can accurately predict PAS, placenta percreta, and adverse peripartum outcome.
These proposed MRI-based scoring models could help accurately predict PAS invasiveness and provide evidence-based risk stratification in the management of high-risk pregnant women for PAS.
• Accurately identifying placenta accreta spectrum (PAS) and assessing its invasiveness depending solely on individual MRI signs remained challenging. • MRI-based scoring models, established through quantitative meta-analysis of multiple MRI signs, offered the potential to predict PAS invasiveness in high-risk pregnant women. • These MRI-based models allowed for evidence-based risk stratification in the management of pregnancies suspected of having PAS.
开发和验证基于 MRI 的评分模型,以预测胎盘植入谱系(PAS)的侵袭性。
本回顾性研究包括一个推导队列和一个验证队列。推导队列来自对评估 MRI 征象在高危妇女中 PAS 和/或胎盘植入的诊断性能的已发表研究的系统评价。确定了显著的征象,并用于开发 PAS 和胎盘植入的预测模型。在 2016 年至 2021 年间,连续接受胎盘 MRI 检查的高危 PAS 孕妇构成了验证队列。两名放射科医生独立评估 MRI 征象。参考标准为术中及病理发现。使用曲线下面积(AUC)评估基于 MRI 的模型的预测能力。
推导队列包括 26 项研究,共纳入 2568 名女性,验证队列由 294 名 PAS 患者组成,其中 258 名患者经手术和病理证实(88%)。定量荟萃分析显示,T2 暗带、胎盘/子宫膨出、T2 低信号界面消失、膀胱壁中断、胎盘不均匀性和异常胎盘内血管分布与 PAS 和胎盘植入均相关,而子宫肌层变薄和局灶性外生性肿块仅与 PAS 相关。PAS 模型的 AUC 为 0.90(95%CI:0.86,0.93),用于预测 PAS,0.85(95%CI:0.79,0.90)用于预测不良围产期结局;胎盘植入模型预测胎盘植入的 AUC 为 0.92(95%CI:0.86,0.98)。
基于定量荟萃分析建立的基于 MRI 的评分模型可以准确预测 PAS、胎盘植入和不良围产期结局。
这些拟议的基于 MRI 的评分模型可帮助准确预测 PAS 的侵袭性,并为高危孕妇 PAS 的管理提供基于证据的风险分层。
• 仅依靠单个 MRI 征象准确识别胎盘植入谱系(PAS)并评估其侵袭性仍然具有挑战性。• 基于多项 MRI 征象的定量荟萃分析建立的基于 MRI 的评分模型,有可能预测高危孕妇 PAS 的侵袭性。• 这些基于 MRI 的模型可在疑似 PAS 孕妇的管理中进行基于证据的风险分层。