■复发性妊娠丢失(RPL)通常与子宫内膜容受性(ER)窗口延长有关,导致无法存活的胚胎植入。现有的ER评估方法在可靠性和侵入性方面面临挑战。医学成像领域的影像组学为ER分析提供了一种非侵入性解决方案,但复杂,RPL中的非线性影像-ER关系需要高级分析。机器学习(ML)为解释这些数据集提供了准确性,尽管将影像组学与ML整合用于RPL中ER评估的研究有限。
■为了开发和验证ML模型,该模型采用了从多模态经阴道超声图像得出的影像组学特征,重点改进RPL中的ER评估。
■这次回顾展,对照研究分析了346例原因不明的RPL患者和369例对照的数据.参与者被分为训练和测试队列,用于模型开发和准确性验证,分别。从灰度(GS)和剪切波弹性成像(SWE)图像得出的放射学特征,在植入窗口期间获得的,经历了一个全面的五步选择过程。五个ML分类器,每个人都接受了放射学的训练,临床,或组合数据集,接受了RPL风险分层培训。选择在鉴定RPL患者方面表现最高的模型用于使用测试队列进一步验证。通过应用Shapley加性解释(SHAP)分析,增强了该最佳模型的可解释性。
■训练队列分析(242RPL,258名对照)确定了与RPL风险相关的9个关键影像学特征。极端梯度提升(XGBoost)模型,结合放射学和临床数据,表现出优越的辨别能力。这通过其0.871的曲线下面积(AUC)评分证明,优于其他ML分类器。215名受试者的测试队列中的验证(104RPL,111个对照)确认了其准确性(AUC:0.844)和一致性。SHAP分析确定了四个子宫内膜SWE特征和两个GS特征,以及年龄等临床变量,SAPI,VI,作为RPL风险分层的关键决定因素。
■在WOI期间将ML与来自多模式子宫内膜超声的影像组学相结合,可有效识别RPL患者。XGBoost模型,合并放射学和临床数据,提供了一种非侵入性的,RPL管理的准确方法,显著加强诊断和治疗。
UNASSIGNED: Recurrent pregnancy loss (RPL) frequently links to a prolonged endometrial receptivity (ER) window, leading to the implantation of non-viable embryos. Existing ER assessment methods face challenges in reliability and invasiveness. Radiomics in medical imaging offers a non-invasive solution for ER analysis, but complex, non-linear radiomic-ER relationships in RPL require advanced analysis. Machine learning (ML) provides precision for interpreting these datasets, although research in integrating radiomics with ML for ER evaluation in RPL is limited.
UNASSIGNED: To develop and validate an ML model that employs radiomic features derived from multimodal transvaginal ultrasound images, focusing on improving ER evaluation in RPL.
UNASSIGNED: This retrospective, controlled study analyzed data from 346 unexplained RPL patients and 369 controls. The participants were divided into training and testing cohorts for model development and accuracy validation, respectively. Radiomic features derived from grayscale (GS) and shear wave elastography (SWE) images, obtained during the window of implantation, underwent a comprehensive five-step selection process. Five ML classifiers, each trained on either radiomic, clinical, or combined datasets, were trained for RPL risk stratification. The model demonstrating the highest performance in identifying RPL patients was selected for further validation using the testing cohort. The interpretability of this optimal model was augmented by applying Shapley additive explanations (SHAP) analysis.
UNASSIGNED: Analysis of the training cohort (242 RPL, 258 controls) identified nine key radiomic features associated with RPL risk. The extreme gradient boosting (XGBoost) model, combining radiomic and clinical data, demonstrated superior discriminatory ability. This was evidenced by its area under the curve (AUC) score of 0.871, outperforming other ML classifiers. Validation in the testing cohort of 215 subjects (104 RPL, 111 controls) confirmed its accuracy (AUC: 0.844) and consistency. SHAP analysis identified four endometrial SWE features and two GS features, along with clinical variables like age, SAPI, and VI, as key determinants in RPL risk stratification.
UNASSIGNED: Integrating ML with radiomics from multimodal endometrial ultrasound during the WOI effectively identifies RPL patients. The XGBoost model, merging radiomic and clinical data, offers a non-invasive, accurate method for RPL management, significantly enhancing diagnosis and treatment.