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胎盘MRI弥散衍生血管密度及形态特征在诊断胎盘植入谱系疾病中的相关性

The relevance of placenta MRI diffusion-derived vessels density and morphological features in diagnosing placenta accreta spectrum.

作者信息

Lu Tao, Yao Dian-Qi, Xiao Ben-Heng, Sheng Wei, Chen Meining, Wáng Yì Xiáng J

机构信息

Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.

Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, New Territories, Hong Kong SAR, China.

出版信息

BMC Med Imaging. 2025 May 22;25(1):180. doi: 10.1186/s12880-025-01735-9.

DOI:10.1186/s12880-025-01735-9
PMID:40405115
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12100846/
Abstract

OBJECTIVES

MRI morphological features are postulated to represent PAS (placenta accreta spectrum), while diffusion-derived vessel density (DDVD) computed from a simple diffusion MRI protocol is a new imaging biomarker representing perfusion. This study aims to explore the relevance of DDVD and MRI morphological features in diagnosing PAS.

METHODS

We enrolled 86 patients with PAS and 40 pregnant women without PAS. Each case underwent intravoxel incoherent motion (IVIM) MRI sequence with 11 b-values. DDVD was the signal difference between b = 0 and b = 50 s/mm images. DDVDr of the placenta or the placental-myometrial interface was calculated as: [lower 1/3 placenta DDVD]/ [upper 2/3 placenta DDVD], respectively. Differences of DDVDr in non-PAS pregnancies and in mild or severe PAS patients were explored using Kruskal-Wallis H-test. χ2 test was used to assess the predictive ability of MRI morphological features in diagnosing all PAS and severe PAS. Receiver operating characteristics (ROC) analysis was used to estimate the discriminative ability of DDVDr and MRI morphological features for all PAS and severe PAS.

RESULTS

DDVDr(placenta) and DDVDr(interface) were significantly higher in patients with severe PAS (median, 1.194 and 1.230) than those in non-PAS (1.024 and 1.016, both p < 0.001). T2 dark bands and abnormal intraplacental vascularity were identified as predictors for all-inclusive PAS and severe PAS. DDVDr(interface) was significantly higher in patients manifested with T2 dark bands and abnormal intraplacental vascularity (median, 1.242 vs. 1.116, 1.230 vs. 1.126 p < 0.05). When DDVDr(interface) and the MRI morphological features were combined for predicting all-inclusive PAS and severe PAS, the AUC reached 0.894 and 0.916, respectively.

CONCLUSION

The combination of placenta MRI diffusion-derived vessels density and qualitative can be used for the accurate prediction of PAS.

摘要

目的

磁共振成像(MRI)形态学特征被认为可代表胎盘植入谱系(PAS),而通过简单扩散MRI协议计算得出的扩散衍生血管密度(DDVD)是一种代表灌注的新型成像生物标志物。本研究旨在探讨DDVD与MRI形态学特征在诊断PAS中的相关性。

方法

我们纳入了86例PAS患者和40例无PAS的孕妇。每例患者均接受具有11个b值的体素内不相干运动(IVIM)MRI序列检查。DDVD为b = 0和b = 50 s/mm图像之间的信号差异。胎盘或胎盘 - 肌层界面的DDVDr分别计算为:[胎盘下1/3的DDVD]/[胎盘上2/3的DDVD]。使用Kruskal - Wallis H检验探讨非PAS妊娠以及轻度或重度PAS患者中DDVDr的差异。χ2检验用于评估MRI形态学特征在诊断所有PAS和重度PAS中的预测能力。采用受试者操作特征(ROC)分析来估计DDVDr和MRI形态学特征对所有PAS和重度PAS的鉴别能力。

结果

重度PAS患者的DDVDr(胎盘)和DDVDr(界面)(中位数分别为1.194和1.230)显著高于非PAS患者(分别为1.024和1.016,均p < 0.001)。T2低信号带和胎盘内血管异常被确定为所有PAS和重度PAS的预测指标。表现出T2低信号带和胎盘内血管异常的患者的DDVDr(界面)显著更高(中位数分别为1.242对1.116,1.230对1.126,p < 0.05)。当将DDVDr(界面)和MRI形态学特征结合用于预测所有PAS和重度PAS时,曲线下面积(AUC)分别达到0.894和0.916。

结论

胎盘MRI扩散衍生血管密度与定性特征相结合可用于准确预测PAS。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91aa/12100846/69a96b008ff8/12880_2025_1735_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91aa/12100846/c7c91fe3a6f9/12880_2025_1735_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91aa/12100846/b52fc438cb65/12880_2025_1735_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91aa/12100846/69a96b008ff8/12880_2025_1735_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91aa/12100846/c7c91fe3a6f9/12880_2025_1735_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91aa/12100846/b52fc438cb65/12880_2025_1735_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91aa/12100846/afd4bda6fbf7/12880_2025_1735_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91aa/12100846/69a96b008ff8/12880_2025_1735_Fig4_HTML.jpg

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