基于磁共振T2WI影像组学模型对胎盘植入性疾病进行产前诊断及分型  

Radiomics model based on MR T2WI for prenatal diagnosis and classification of placenta accreta spectrum disorders

作　　者：邹锦莉 胡振远 王新莲[1] 王克扬[1] 魏炜 解立志 梁宇霆[1] ZOU Jinli;HU Zhenyuan;WANG Xinlian;WANG Keyang;WEI Wei;XIE Lizhi;LIANG Yuting(Department of Radiology,Beijing Obstetrics and Gynecology Hospital,Capital Medical University/Beijing Maternal and Child Health Care Hospital,Beijing 100006,China;School of Electronics and information,Xi'an Polytechnic University,Xi'an 710048,China;Beijing Magnetic Resonance Products Department,GE Medical Systems Trade&Development(Shanghai)Co.,Ltd.,Beijing 100176,China)

机构地区：[1]首都医科大学附属北京妇产医院/北京妇幼保健院放射科,北京100006 [2]西安工程大学电子信息学院,西安710048 [3]通用电气医疗系统贸易发展(上海)有限公司北京磁共振产品部,北京100176

出　　处：《磁共振成像》2024年第1期137-144,共8页Chinese Journal of Magnetic Resonance Imaging

基　　金：首都卫生发展科研专项(编号:首发2022-2-2117);陕西省自然科学基础研究计划项目(编号:2023-JC-YB-682)。

摘　　要：目的探讨基于磁共振T2WI的影像组学模型在产前预测胎盘植入性疾病(placenta accreta spectrum disorders,PAS)及其亚型的应用价值。材料与方法回顾性分析了2018年1月至2023年1月在北京妇产医院住院分娩的193例单胎妊娠孕妇数据,其中PAS 134例,非PAS 59例,所有患者根据同一分型的总数按2∶1的比例随机划分为训练集和测试集。在T2WI序列图像提取影像组学特征,Pearson相关系数和最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归用于特征筛选,基于筛选后的特征构建PAS预测模型。然后,计算便于临床应用的影像组学评分评估PAS分型,使用单因素分析与多因素分析进一步分析其他潜在的临床危险因素,包括年龄、孕周、此前孕次、此前产次、此前剖宫产次数、胎盘问题(前置胎盘)和既往子宫手术史,选择临床主要风险因素建立基于影像组学评分和临床特征的临床-影像组学模型并绘制诺莫图。通过受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)评估模型的预测性能,采用DeLong检验比较模型间的预测效能,校准曲线用于评估预测模型的校准程度,决策曲线用于评估预测模型临床价值。结果在T2WI序列图像上提取了806个影像组学特征,经过Pearson相关分析后保留147个影像组学特征,经LASSO回归处理后筛选出10个影像组学特征,基于影像组学特征构建影像组学模型。影像组学模型的训练集AUC值为0.933(95%CI:0.888~0.978),准确率为88.37%,敏感度为88.78%,特异度为87.10%,阳性预测值(positive predictive value,PPV)为95.60%,阴性预测值(negative predictive value,NPV)为71.05%;测试集AUC值为0.914(95%CI:0.835~0.993),准确率为89.06%,敏感度为90.91%,特异度为85.00%,PPV为90.00%,NPV为80.95%。校准曲线和决策曲线表明模型具有较高性能和潜在临床应用价值。影像组学评分对穿透性胎盘植�Objective:To investigate the application value of radiomics model based on MR T2WI for prenatal predicting placenta accreta spectrum disorders(PAS)and determining the subtype of PAS.Materials and Methods:The data of 193 pregnant women with singleton pregnancies who were hospitalized for delivery in Beijing Obstetrics and Gynecology Hospital from January 2018 to January 2023 were retrospectively analyzed,including 134 cases of PAS and 59 cases of non-PAS.All pregnant women were randomly divided into training set and test set in a 2∶1 ratio based on the total number of patients with the same subtype.The radiomics features were extracted from T2WI sequence,Pearson correlation coefficient and least absolute shrinkage and selection operator(LASSO)regression were used for feature screening,and the radiomics models for predicting PAS were constructed.Then,a radiomics scoring system for clinical application is constructed and trained to evaluate the subtypes of PAS,and univariate analysis and multivariate analysis are used to further analyze other potential clinical risk factors,including age,gestational age,previous gravidity,previous parity,the history of cesarean section,placental problems(placenta previa),and the history of uterine-related operations.Establish a nomogram based on the selection of clinical major risk factors.The receiver operating characteristic(ROC)curve was drawn to evaluate the predictive performance of the model,and DeLong test was used to compare the predictive efficiency of these models,the calibration curve is used to evaluate the degree of calibration of the prediction model,and the decision curve is used to evaluate the clinical value of the prediction model.Results:806 radiomics features were extracted from T2WI sequence,147 radiomics features were retained after Pearson correlation analysis,and 10 radiomics features were screened out after LASSO regression processing,and a radiomics model that is applied to scoring was established.The area under the curve(AUC)value of the radiomics model

关 键 词：胎盘疾病 胎盘植入性疾病 产前诊断 影像组学 磁共振成像 

分 类 号：R445.2[医药卫生—影像医学与核医学] R714.2[医药卫生—诊断学]