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基于 MRI 影像组学-临床的列线图模型对胎盘部位滋养细胞肿瘤谱疾病进行产前预测。

MRI-radiomics-clinical-based nomogram for prenatal prediction of the placenta accreta spectrum disorders.

机构信息

Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, No. 63 Duobao Road, Guangzhou, 510000, People's Republic of China.

Guangzhou Institute of Obstetrics & Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, No. 63 Duobao Road, Guangzhou, 510000, People's Republic of China.

出版信息

Eur Radiol. 2022 Nov;32(11):7532-7543. doi: 10.1007/s00330-022-08821-4. Epub 2022 May 19.

Abstract

OBJECTIVES

To investigate whether an MRI-radiomics-clinical-based nomogram can be used to prenatal predict the placenta accreta spectrum (PAS) disorders.

METHODS

The pelvic MR images and clinical data of 156 pregnant women with pathologic-proved PAS (PAS group) and 115 pregnant women with no PAS (non-PAS group) identified by clinical and prenatal ultrasonic examination were retrospectively collected from two centers. These pregnancies were divided into a training (n = 133), an independent validation (n = 57), and an external validation (n = 81) cohort. Radiomic features were extracted from images of transverse oblique T2-weighted imaging. A radiomics signature was constructed. A nomogram, composed of MRI morphological findings, radiomic features, and prenatal clinical characteristics, was developed. The discrimination and calibration of the nomogram were conducted to assess its performance.

RESULTS

A radiomics signature, including three PAS-related features, was associated with the presence of PAS in the three cohorts (p < 0.001 to p = 0.001). An MRI-radiomics-clinical nomogram incorporating radiomics signature, two prenatal clinical features, and two MRI morphological findings was developed, yielding a higher area under the curve (AUC) than that of the MRI morphological-determined PAS in the training cohort (0.89 vs. 0.78; p < 0.001) and external validation cohort (0.87 vs. 0.75; p = 0.003), while a comparable AUC value in the validation cohort (0.91 vs. 0.81; p = 0.09). The calibration was good.

CONCLUSIONS

An MRI-radiomics-clinical nomogram had a robust performance in antenatal predicting the PAS in pregnancies.

KEY POINTS

• An MRI-radiomics-clinical-based nomogram might serve as an adjunctive approach for the treatment decision-making in pregnancies suspicious of PAS. • The radiomic score provides a mathematical formula that predicts the possibility of PAS by using the MRI data, and pregnant women with PAS had higher radiomic scores than those without PAS.

摘要

目的

研究 MRI 放射组学-临床为基础的列线图是否可用于产前预测胎盘植入谱(PAS)疾病。

方法

回顾性收集了来自两个中心的 156 例经病理证实的 PAS(PAS 组)和 115 例经临床和产前超声检查无 PAS(非 PAS 组)孕妇的盆腔 MRI 图像和临床资料。这些妊娠分为训练(n=133)、独立验证(n=57)和外部验证(n=81)队列。从横斜 T2 加权成像的图像中提取放射组学特征。构建放射组学特征。构建了一个由 MRI 形态学发现、放射组学特征和产前临床特征组成的列线图。对列线图进行了判别和校准,以评估其性能。

结果

三个与 PAS 相关的放射组学特征组成的放射组学特征与三个队列中 PAS 的存在相关(p<0.001 至 p=0.001)。纳入放射组学特征、两个产前临床特征和两个 MRI 形态学发现的 MRI 放射组学-临床列线图,在训练队列(0.89 比 0.78;p<0.001)和外部验证队列(0.87 比 0.75;p=0.003)中,曲线下面积(AUC)高于 MRI 形态学确定的 PAS,而在验证队列中 AUC 值相当(0.91 比 0.81;p=0.09)。校准良好。

结论

MRI 放射组学-临床列线图在产前预测 PAS 方面具有良好的性能。

关键点

  1. MRI 放射组学-临床为基础的列线图可能成为疑似 PAS 妊娠治疗决策的辅助方法。

  2. 放射组评分提供了一个数学公式,通过 MRI 数据预测 PAS 的可能性,患有 PAS 的孕妇的放射组评分高于没有 PAS 的孕妇。

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