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使用MRI影像组学特征预测胎盘植入谱系疾病和子宫切除术

Placenta Accreta Spectrum and Hysterectomy Prediction Using MRI Radiomic Features.

作者信息

Leitch Ka'Toria, Shahedi Maysam, Dormer James D, Do Quyen N, Xi Yin, Lewis Matthew A, Herrera Christina L, Spong Catherine Y, Madhuranthakam Ananth J, Twickler Diane M, Fei Baowei

机构信息

Department of Bioengineering, The University of Texas at Dallas, Richardson, TX.

Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX.

出版信息

Proc SPIE Int Soc Opt Eng. 2022 Feb-Mar;12033. doi: 10.1117/12.2611587. Epub 2022 Apr 4.

Abstract

In women with placenta accreta spectrum (PAS), patient management may involve cesarean hysterectomy at delivery. Magnetic resonance imaging (MRI) has been used for further evaluation of PAS and surgical planning. This work tackles two prediction problems: predicting presence of PAS and predicting hysterectomy using MR images of pregnant patients. First, we extracted approximately 2,500 radiomic features from MR images with two regions of interest: the placenta and the uterus. In addition to analyzing two regions of interest, we dilated the placenta and uterus masks by 5, 10, 15, and 20 mm to gain insights from the myometrium, where the uterus and placenta overlap in the case of PAS. This study cohort includes 241 pregnant women. Of these women, 89 underwent hysterectomy while 152 did not; 141 with suspected PAS, and 100 without suspected PAS. We obtained an accuracy of 0.88 for predicting hysterectomy and an accuracy of 0.92 for classifying suspected PAS. The radiomic analysis tool is further validated, it can be useful for aiding clinicians in decision making on the care of pregnant women.

摘要

在患有胎盘植入谱系疾病(PAS)的女性中,患者管理可能包括分娩时进行剖宫产子宫切除术。磁共振成像(MRI)已被用于对PAS进行进一步评估和手术规划。这项工作解决了两个预测问题:使用孕妇的MR图像预测PAS的存在以及预测子宫切除术。首先,我们从具有两个感兴趣区域(胎盘和子宫)的MR图像中提取了大约2500个放射组学特征。除了分析两个感兴趣区域外,我们还将胎盘和子宫掩码分别扩张了5、10、15和20毫米,以便从子宫肌层获取见解,在PAS病例中子宫和胎盘在子宫肌层重叠。该研究队列包括241名孕妇。在这些女性中,89人接受了子宫切除术,152人未接受;141人疑似患有PAS,100人未疑似患有PAS。我们预测子宫切除术的准确率为0.88,对疑似PAS进行分类的准确率为0.92。放射组学分析工具得到了进一步验证,它有助于临床医生对孕妇护理做出决策。

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