BACKGROUND Identification of pregnancies with postpartum haemorrhage (PPH) antenatally rather than intrapartum would aid delivery planning, facilitate transfusion requirements and decrease maternal complications. MRI has been increasingly used for placenta evaluation. Here, we aim to build a nomogram incorporating both clinical and radiomic features of placenta to predict the risk for PPH in pregnancies during caesarian delivery (CD). METHODS A total of 298 pregnant women were retrospectively enrolled from Henan Provincial People's Hospital (training cohort: n = 207) and from The Third Affiliated Hospital of Zhengzhou University (external validation cohort: n = 91). These women were suspected with placenta accreta spectrum (PAS) disorders and underwent MRI for placenta evaluation. All of them underwent CD and were singleton. PPH was defined as more than 1000 mL estimated blood loss (EBL) during CD. Radiomic features were selected based on their correlations with EBL. Radiomic, clinical, radiological, clinicoradiological and clinicoradiomic models were built to predict the risk of PPH for each patient. The model with the best prediction performance was validated with its discrimination ability, calibration curve and clinical application. FINDINGS Thirty-five radiomic features showed strong correlation with EBL. The clinicoradiomic model resulted in the best discrimination ability for risk prediction of PPH, with AUC of 0.888 (95% CI, 0.844-0.933) and 0.832 (95% CI, 0.746-0.913), sensitivity of 91.2% (95% CI, 85.8%-96.7%) and 97.6% (95% CI, 92.7%-100%) in the training and validation cohort respectively. For patients with severe PPH (EBL more than 2000 mL), 53 out of 55 pregnancies (96.4%) in the training cohort and 18 out of 18 (100%) pregnancies in the validation cohort were identified by the clinicoradiomic model. The model performed better in patients without placenta previa (PP) than in patients with PP, with AUC of 0.983 compared with 0.867, sensitivity of 100% compared with 90.8% in the training cohort, AUC of 0.832 compared with 0.815, sensitivity of 97.6% compared with 97.2% in the validation cohort. INTERPRETATION The clinicoradiomic model incorporating both prenatal clinical factors and radiomic signature of placenta on T2WI showed good performance for risk prediction of PPH. The predictive model can identify severe PPH with high sensitivity and can be applied in patients with and without PP.

中文翻译：

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T2WI 胎盘放射组学分析有助于预测产后出血：一项多中心研究。

背景 在产前而非产时识别妊娠是否患有产后出血 (PPH) 将有助于制定分娩计划、促进输血需求并减少孕产妇并发症。MRI 越来越多地用于胎盘评估。在这里，我们的目标是建立一个结合胎盘临床和影像学特征的列线图，以预测剖腹产 (CD) 期间妊娠发生 PPH 的风险。方法回顾性纳入河南省人民医院（训练队列：n = 207）和郑州大学第三附属医院（外部验证队列：n = 91）的298名孕妇。这些女性被怀疑患有植入性胎盘谱系 (PAS) 疾病，并接受了 MRI 进行胎盘评估。他们都接受了 CD 并且都是单身。PPH 定义为 CD 期间估计失血量 (EBL) 超过 1000 mL。放射组学特征是根据其与 EBL 的相关性来选择的。建立放射组学、临床、放射学、临床放射学和临床放射组学模型来预测每位患者的 PPH 风险。通过判别能力、校准曲线和临床应用验证了预测性能最佳的模型。结果 35 项放射组学特征显示与 EBL 具有很强的相关性。临床放射组学模型对 PPH 风险预测的区分能力最佳，AUC 分别为 0.888（95% CI，0.844-0.933）和 0.832（95% CI，0.746-0.913），敏感性为 91.2%（95% CI，85.8）训练和验证队列中的比例分别为 %-96.7%) 和 97.6% (95% CI, 92.7%-100%)。对于严重 PPH 患者（EBL 超过 2000 mL），临床放射组学模型识别出训练队列中 55 例妊娠中的 53 例（96.4%）和验证队列中 18 例妊娠中的 18 例（100%）。该模型在无前置胎盘（PP）患者中的表现优于有 PP 患者，训练队列中 AUC 为 0.983 与 0.867，敏感性为 100% 与 90.8%，AUC 为 0.832 与 0.815，敏感性为 97.6%相比之下，验证组中的这一比例为 97.2%。解释 结合产前临床因素和 T2WI 胎盘放射组学特征的临床放射组学模型在 PPH 风险预测方面表现出良好的性能。该预测模型可以高灵敏度地识别严重的 PPH，并且可以应用于患有或不患有 PP 的患者。