Ren Hainan, Mori Naoko, Mugikura Shunji, Shimizu Hiroaki, Kageyama Sakiko, Saito Masatoshi, Takase Kei
Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
Division of Image Statistics, Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
Abdom Radiol (NY). 2021 Nov;46(11):5344-5352. doi: 10.1007/s00261-021-03226-1. Epub 2021 Jul 30.
To separately perform visual and texture analyses of the axial, coronal, and sagittal planes of T2-weighted images and identify the optimal method for differentiating between the normal placenta and placenta accreta spectrum (PAS).
Eighty consecutive patients (normal group, n = 50; PAS group, n = 30) underwent preoperative MRI. A scoring system (0-2) was used to evaluate the degree of abnormality observed in visual analysis (bulging, abnormal vascularity, T2 dark band, placental heterogeneity). The axial, coronal, and sagittal planes were manually segmented separately to obtain texture features, and seven combinations were obtained: axial; coronal; sagittal; axial and coronal; axial and sagittal; coronal and sagittal; and axial, coronal, and sagittal. Feature selection using the least absolute shrinkage and selection operator method and model construction using a support vector machine algorithm with k-fold cross-validation were performed. AUC was used to evaluate diagnostic performance.
The AUC of visual analysis was 0.75. The model 'coronal and sagittal' had the highest AUC (0.98) amongst the seven combinations. The fivefold cross-validation for the model 'coronal and sagittal' showed AUCs of 0.85 and 0.97 in training and validation sets, respectively. The AUC of the model 'coronal and sagittal' for all subjects was significantly higher than that of visual analysis (0.98 vs. 0.75; p < 0.0001).
The model 'coronal and sagittal' can accurately differentiate between the normal placenta and PAS, with a significantly better diagnostic performance than visual analysis. Texture analysis is an optimal method for differentiating between the normal placenta and PAS.
分别对T2加权图像的轴位、冠状位和矢状位进行视觉和纹理分析，确定区分正常胎盘和胎盘植入谱系（PAS）的最佳方法。
连续80例患者（正常组，n = 50；PAS组，n = 30）术前行MRI检查。采用评分系统（0 - 2分）评估视觉分析中观察到的异常程度（膨出、异常血管、T2低信号带、胎盘异质性）。分别手动分割轴位、冠状位和矢状位以获得纹理特征，得到七种组合：轴位；冠状位；矢状位；轴位和冠状位；轴位和矢状位；冠状位和矢状位；轴位、冠状位和矢状位。采用最小绝对收缩和选择算子法进行特征选择，并使用支持向量机算法和k折交叉验证进行模型构建。采用AUC评估诊断性能。
视觉分析的AUC为0.75。在七种组合中，“冠状位和矢状位”模型的AUC最高（0.98）。“冠状位和矢状位”模型的五折交叉验证显示，训练集和验证集的AUC分别为0.85和0.97。所有受试者的“冠状位和矢状位”模型的AUC显著高于视觉分析（0.98对0.755；p < 0.0001）。
“冠状位和矢状位”模型能够准确区分正常胎盘和PAS，诊断性能明显优于视觉分析。纹理分析是区分正常胎盘和PAS的最佳方法。
