Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China.
Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
Abdom Radiol (NY). 2024 Jul;49(7):2325-2339. doi: 10.1007/s00261-024-04419-0. Epub 2024 Jun 19.
To develop and validate a nomogram model that combines radiomics features, clinical factors, and coagulation function indexes (CFI) to predict intraoperative blood loss (IBL) during cesarean sections, and to explore its application in optimizing perioperative management and reducing maternal morbidity.
In this retrospective consecutive series study, a total of 346 patients who underwent magnetic resonance imaging (156 for training and 68 for internal test, center 1; 122 for external test, center 2) were included. IBL+ was defined as more than 1000 mL estimated blood loss during cesarean sections. The prediction models of IBL were developed based on machine-learning algorithms using CFI, radiomics features, and clinical factors. ROC analysis was performed to evaluate the performance for IBL diagnosis.
The support vector machine model incorporating all three modalities achieved an AUC of 0.873 (95% CI 0.769-0.941) and a sensitivity of 1.000 (95% CI 0.846-1.000) in the internal test set, with an AUC of 0.806 (95% CI 0.725-0.872) and a sensitivity of 0.873 (95% CI 0.799-0.922) in the external test set. It was also scored significantly higher than the CFI model (P = 0.035) on the internal test set, and both the CFI (P = 0.002) and radiomics-CFI models (P = 0.007) on the external test set. Additionally, the nomogram constructed based on three modalities achieved an internal testing set AUC of 0.960 (95% CI 0.806-0.999) and an external testing set AUC of 0.869 (95% CI 0.684-0.967) in the pregnant population without a pernicious placenta previa. It is noteworthy that the AUC of the proposed model did not show a statistically significant improvement compared to the Clinical-CFI model in both internal (P = 0.115) and external test sets (P = 0.533).
The proposed model demonstrated good performance in predicting intraoperative blood loss (IBL), exhibiting high sensitivity and robust generalizability, with potential applicability to other surgeries such as vaginal delivery and postpartum hysterectomy. However, the performance of the proposed model was not statistically significantly better than that of the Clinical-CFI model.
开发并验证一种列线图模型，该模型结合放射组学特征、临床因素和凝血功能指标（CFI），以预测剖宫产术中的术中出血量（IBL），并探讨其在优化围手术期管理和降低产妇发病率方面的应用。
本回顾性连续系列研究共纳入 346 例接受磁共振成像检查的患者（训练组 156 例，内部测试中心 1 组 68 例；外部测试中心 2 组 122 例）。IBL+定义为剖宫产术中出血量超过 1000 mL。使用 CFI、放射组学特征和临床因素，基于机器学习算法开发 IBL 预测模型。采用 ROC 分析评估 IBL 诊断的性能。
纳入所有三种模态的支持向量机模型在内部测试集中的 AUC 为 0.873（95%CI 0.769-0.941），敏感度为 1.000（95%CI 0.846-1.000），在外部测试集中的 AUC 为 0.806（95%CI 0.725-0.872），敏感度为 0.873（95%CI 0.799-0.922）。在内部测试集中，该模型的评分也明显高于 CFI 模型（P=0.035），在外部测试集中，CFI（P=0.002）和放射组学-CFI 模型（P=0.007）也均高于 CFI 模型。此外，在没有凶险性前置胎盘的孕妇人群中，基于三种模态构建的列线图在内部测试集中的 AUC 为 0.960（95%CI 0.806-0.999），在外部测试集中的 AUC 为 0.869（95%CI 0.684-0.967）。值得注意的是，与内部（P=0.115）和外部测试集（P=0.533）中的临床-CFI 模型相比，所提出模型的 AUC 并没有显示出统计学上的显著改善。
该模型在预测术中出血量（IBL）方面表现出良好的性能，具有较高的敏感度和稳健的泛化能力，可能适用于阴道分娩和产后子宫切除术等其他手术。然而，与临床-CFI 模型相比，该模型的性能并没有统计学上的显著提高。
