Zhang Jieying, Zhang Qi, Wang Tingting, Song Yan, Yu Xiaoduo, Xie Lizhi, Chen Yan, Ouyang Han
Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Front Oncol. 2022 May 27;12:887546. doi: 10.3389/fonc.2022.887546. eCollection 2022.
To develop and validate a radiomics model based on multimodal MRI combining clinical information for preoperative distinguishing concurrent endometrial carcinoma (CEC) from atypical endometrial hyperplasia (AEH).
A total of 122 patients (78 AEH and 44 CEC) who underwent preoperative MRI were enrolled in this retrospective study. Radiomics features were extracted based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. After feature reduction by minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithm, single-modal and multimodal radiomics signatures, clinical model, and radiomics-clinical model were constructed using logistic regression. Receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis were used to assess the models.
The combined radiomics signature of T2WI, DWI, and ADC maps showed better discrimination ability than either alone. The radiomics-clinical model consisting of multimodal radiomics features, endometrial thickness >11mm, and nulliparity status achieved the highest area under the ROC curve (AUC) of 0.932 (95% confidential interval [CI]: 0.880-0.984), bootstrap corrected AUC of 0.922 in the training set, and AUC of 0.942 (95% CI: 0.852-1.000) in the validation set. Subgroup analysis further revealed that this model performed well for patients with preoperative endometrial biopsy consistent and inconsistent with postoperative pathologic data (consistent group, F1-score = 0.865; inconsistent group, F1-score = 0.900).
The radiomics model, which incorporates multimodal MRI and clinical information, might be used to preoperatively differentiate CEC from AEH, especially for patients with under- or over-estimated preoperative endometrial biopsy.
基于多模态磁共振成像（MRI）结合临床信息开发并验证一种用于术前鉴别同时性子宫内膜癌（CEC）与非典型子宫内膜增生（AEH）的放射组学模型。
本回顾性研究纳入了122例术前行MRI检查的患者（78例AEH和44例CEC）。基于T2加权成像（T2WI）、扩散加权成像（DWI）和表观扩散系数（ADC）图提取放射组学特征。通过最小冗余最大相关和最小绝对收缩与选择算子算法进行特征降维后，使用逻辑回归构建单模态和多模态放射组学特征、临床模型以及放射组学 - 临床模型。采用受试者操作特征（ROC）分析、校准曲线和决策曲线分析对模型进行评估。
T2WI、DWI和ADC图的联合放射组学特征表现出比单独使用更好的鉴别能力。由多模态放射组学特征、子宫内膜厚度>11mm和未生育状态组成的放射组学 - 临床模型在ROC曲线下面积（AUC）最高，为0.932（95%置信区间[CI]：0.880 - 0.984），训练集中经自助法校正后的AUC为0.922，验证集中的AUC为0.942（95%CI：0.852 - 1.000）。亚组分析进一步显示，该模型在术前子宫内膜活检结果与术后病理数据一致和不一致的患者中均表现良好（一致组，F1评分 = 0.865；不一致组，F1评分 = 0.900）。
结合多模态MRI和临床信息的放射组学模型可用于术前区分CEC与AEH，尤其适用于术前子宫内膜活检估计过低或过高的患者。
