| 孔伟,余裕珍,王康,陈龙,胡运祥,陈卫国.术前MRI影像组学模型预测子宫内膜癌风险分层[J].中国医学影像技术,2023,39(12):1857~1861 |
| 术前MRI影像组学模型预测子宫内膜癌风险分层 |
| Preoperative MRI radiomics models for predicting risk stratification of endometrial cancer |
| 投稿时间：2023-07-11 修订日期：2023-08-27 |
| DOI：10.13929/j.issn.1003-3289.2023.12.026 |
| 中文关键词: 子宫内膜肿瘤 磁共振成像 影像组学 |
| 英文关键词:endometrial neoplasms magnetic resonance imaging radiomics |
| 基金项目: |
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| 中文摘要: |
| 目的 观察术前MRI影像组学模型预测子宫内膜癌（EC）风险分层的价值。方法 回顾性分析219例术前接受盆腔MR检查的EC患者，根据术后病理结果将其分为高风险组（n=104）及低风险组（n=115）；按照不同检查时间将患者分别归入训练集（n=153）或测试集（n=66），并于集内划分亚组。以3D Slicer软件于MRI中手动勾画ROI，分别基于轴位、矢状位脂肪抑制（FS） T2WI及轴位、矢状位增强FS-T1WI中提取1 130个特征，之后以最小绝对收缩和选择算子（LASSO）算法分别选出12、14、16及12个（共54个）影像组学特征（联合MRI特征）；再以LASSO降维并筛选出25个特征（联合LASSO特征）。以极度随机树算法分别基于各序列特征、联合MRI特征及联合LASSO特征构建模型；绘制受试者工作特征曲线，以曲线下面积（AUC）、准确度及F1评分评估各模型预测效能；以各模型在测试集中的AUC及主观阅片的AUC评估其预测效能。结果 训练集中，联合MRI模型与联合LASSO模型的准确率（0.784、0.777）、F1评分（0.730、0.731）及AUC （0.835、0.855）均高于各单一序列模型；其在测试集的敏感度（0.794、0.882）、特异度（0.909、0.969）及AUC （0.904、0.934）均高于主观阅片及各单一序列模型；联合LASSO模型预测效能优于联合MRI模型。结论 术前MRI影像组学可有效预测EC风险分层，尤以联合LASSO模型预测效能最佳。 |
| 英文摘要: |
| Objective To observe the value of preoperative MRI radiomics models for predicting risk stratification of endometrial cancer (EC). Methods Data of 219 EC patients who underwent pelvic MR examination before surgery were retrospectively analyzed. The patients were divided into high risk group (n=104) or low risk group (n=115) according to postoperative pathological findings, also assigned into training set (n=153) or test set (n=66) according to examination time and further divided into high or low risk subgroups in each set. ROI was manually sketched on MRI using 3D Slicer, and each 1 130 features were extracted from axial and sagittal fat suppression (FS) T2WI as well as axial and sagittal enhanced FS-T1WI, respectively. Then the least absolute shrinkage and selection operator (LASSO) was used to select a total of 54 merged MRI features, including 12, 14, 16 and 12 features, respectively. Finally, 25 merged LASSO features were reduced dimensionality and selected by reusing LASSO. Extremely randomized trees algorithm was used to construct radiomics models based on each single sequence features, merged MRI features and merged LASSO features, respectively. Receiver operating characteristic curves were drawn, the area under the curve (AUC), the accuracy and F1 score were obtained to evaluate the predicting efficacy of each model. AUC was used to evaluate the predictive efficacy of the models and subjective diagnosis of test set. Results In training set, the accuracy (0.784, 0.777), F1 score (0.730, 0.731) and AUC (0.835, 0.855) of modelmerged MRI and modelmerged LASSO were both higher than those of each single sequence model, while in test set, the sensitivity (0.794, 0.882), specificity (0.909, 0.969) and AUC (0.904, 0.934) of modelmerged MRI and modelmerged LASSO were both higher than those of subjective diagnosis and each single sequence model. The predictive effiency of modelmerged LASSO was better than that of modelmerged MRI, which was the best model. Conclusion Preoperative MRI radiomics model was effective for predicting risk stratification of endometrial cancer. Modelmerged LASSO had the best performance. |
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