Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, and Sichuan Key Laboratory of Medical Imaging, Nanchong, China.
Magn Reson Med. 2022 Dec;88(6):2621-2632. doi: 10.1002/mrm.29396. Epub 2022 Aug 31.
To develop and validate an MRI-based radiomics model for differentiating invasive placentas in patients with high risks.
A total of 181 pregnant women suspected of placenta accreta spectrum (PAS) disorders and who underwent MRI for placenta evaluation were retrospectively enrolled. The data set was randomly divided into the training (n = 125; invasive = 63, noninvasive = 62) and test (n = 56; invasive = 28, noninvasive = 28) groups. Radiomics features were extracted from half-Fourier acquisition single-shot turbo spin echo (HASTE) and sagittal true fast imaging in steady-state precession (TRUFISP) sequences independently and mainly selected based on their correlations with invasive placentas to construct two radiomics signatures including HASTE-Radscore and TRUFISP-Radscore. Then, the predictive performance of radiomic signatures, clinical features, radiographic features, and their combination were evaluated. The model with the best predictive performance was validated with its discrimination ability, calibration, and clinical usefulness.
Five radiomics features from HASTE and three radiomics features from TRUFISP were retained, respectively, for predicting invasive placentas. The combination of radiomics signatures and clinical features including prior cesarean delivery, placenta previa, and radiographic feature, the placental thickness resulted in the best discrimination ability, with area under the curve of 0.898 (95% confidence interval [CI] 0.844-0.9522) and 0.858 (95% confidence interval 0.7514-0.9655) in the training and test cohort, respectively. The combined model showed a significantly better area under the curve performance and clinical usefulness than independent clinical or radiographic model according to DeLong test (p < .05), net reclassification improvement and integrated discrimination improvement analysis (positive improvement) and decision curve analysis (higher net benefit).
The T -weighted imaging MRI radiomics model could serve as a potential prenatal diagnosis tool for identifying invasive placentas in patients with high risks.
开发并验证一种基于 MRI 的放射组学模型，用于区分高危患者的侵袭性胎盘。
回顾性纳入 181 例疑似胎盘植入谱系（PAS）疾病并接受 MRI 胎盘评估的孕妇。数据集随机分为训练集（n=125；侵袭性=63，非侵袭性=62）和测试集（n=56；侵袭性=28，非侵袭性=28）。从半傅里叶采集单次激发涡轮自旋回波（HASTE）和矢状面真实快速成像稳态进动（TRUFISP）序列中独立提取放射组学特征，主要根据其与侵袭性胎盘的相关性进行选择，以构建两种放射组学特征，包括 HASTE-Radscore 和 TRUFISP-Radscore。然后，评估放射组学特征、临床特征、影像学特征及其组合的预测性能。使用最佳预测性能的模型验证其鉴别能力、校准和临床实用性。
从 HASTE 中保留了 5 个放射组学特征，从 TRUFISP 中保留了 3 个放射组学特征，用于预测侵袭性胎盘。放射组学特征与临床特征（包括既往剖宫产、前置胎盘和影像学特征，胎盘厚度）的组合导致最佳鉴别能力，在训练队列和测试队列中，曲线下面积分别为 0.898（95%置信区间 0.844-0.9522）和 0.858（95%置信区间 0.7514-0.9655）。根据 DeLong 检验（p<0.05）、净重新分类改善和综合判别改善分析（阳性改善）和决策曲线分析（更高的净收益），联合模型的曲线下面积性能和临床实用性均显著优于独立的临床或影像学模型。
T 加权成像 MRI 放射组学模型可作为一种潜在的产前诊断工具，用于识别高危患者的侵袭性胎盘。
