基于 MRI 影像组学分析的 MRI 不可见早期子宫内膜癌的术前评估。
Preoperative Assessment of MRI-Invisible Early-Stage Endometrial Cancer With MRI-Based Radiomics Analysis.
机构信息
Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Department of Gynaecology and Obstetrics, First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
出版信息
J Magn Reson Imaging. 2023 Jul;58(1):247-255. doi: 10.1002/jmri.28492. Epub 2022 Oct 19.
BACKGROUND
Radiomics-based analyses have demonstrated impact on studies of endometrial cancer (EC). However, there have been no radiomics studies investigating preoperative assessment of MRI-invisible EC to date.
PURPOSE
To develop and validate radiomics models based on sagittal T2-weighted images (T2WI) and T1-weighted contrast-enhanced images (T1CE) for the preoperative assessment of MRI-invisible early-stage EC and myometrial invasion (MI).
STUDY TYPE
Retrospective.
POPULATION
One hundred fifty-eight consecutive patients (mean age 50.7 years) with MRI-invisible endometrial lesions were enrolled from June 2016 to March 2022 and randomly divided into the training (n = 110) and validation cohort (n = 48) using a ratio of 7:3.
FIELD STRENGTH/SEQUENCE: 3-T, T2WI, and T1CE sequences, turbo spin echo.
ASSESSMENT
Two radiologists performed image segmentation and extracted features. Endometrial lesions were histopathologically classified as benign, dysplasia, and EC with or without MI. In the training cohort, 28 and 20 radiomics features were selected to build Model 1 and Model 2, respectively, generating rad-score 1 (RS1) and rad-score 2 (RS2) for evaluating MRI-invisible EC and MI.
STATISTICAL TESTS
The least absolute shrinkage and selection operator logistic regression method was used to select radiomics features. Mann-Whitney U tests and Chi-square test were used to analyze continuous and categorical variables. Receiver operating characteristic curve (ROC) and decision curve analysis were used for performance evaluation. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. A P-value <0.05 was considered statistically significant.
RESULTS
Model 1 had good performance for preoperative detecting of MRI-invisible early-stage EC in the training and validation cohorts (AUC: 0.873 and 0.918). In addition, Model 2 had good performance in assessment of MI of MRI-invisible endometrial lesions in the training and validation cohorts (AUC: 0.854 and 0.834).
DATA CONCLUSION
MRI-based radiomics models may provide good performance for detecting MRI-invisible EC and MI.
EVIDENCE LEVEL
3 TECHNICAL EFFICACY: Stage 2.
背景
基于放射组学的分析已证明对子宫内膜癌（EC）的研究有影响。然而，迄今为止，还没有关于使用 MRI 不可见的 EC 进行术前评估的放射组学研究。
目的
开发和验证基于矢状 T2 加权图像（T2WI）和 T1 加权对比增强图像（T1CE）的放射组学模型，用于术前评估 MRI 不可见的早期 EC 和肌层浸润（MI）。
研究类型
回顾性。
人群
本研究共纳入了 2016 年 6 月至 2022 年 3 月期间连续的 158 例 MRI 不可见子宫内膜病变患者（平均年龄 50.7 岁），并使用 7:3 的比例将其随机分为训练队列（n=110）和验证队列（n=48）。
磁场强度/序列：3T、T2WI 和 T1CE 序列，涡轮自旋回波。
评估
两位放射科医生进行图像分割和提取特征。子宫内膜病变经组织病理学分类为良性、发育不良和有或无 MI 的 EC。在训练队列中，分别选择 28 个和 20 个放射组学特征来构建模型 1 和模型 2，为评估 MRI 不可见的 EC 和 MI 生成放射评分 1（RS1）和放射评分 2（RS2）。
统计学检验
使用最小绝对收缩和选择算子逻辑回归方法选择放射组学特征。使用曼-惠特尼 U 检验和卡方检验分析连续和分类变量。使用受试者工作特征曲线（ROC）和决策曲线分析进行性能评估。计算 ROC 曲线下面积（AUC）、准确性、敏感度、特异度、阳性预测值和阴性预测值。P 值<0.05 被认为具有统计学意义。
结果
模型 1 在训练和验证队列中对术前检测 MRI 不可见早期 EC 具有良好的性能（AUC：0.873 和 0.918）。此外，模型 2 在评估 MRI 不可见子宫内膜病变的 MI 方面在训练和验证队列中具有良好的性能（AUC：0.854 和 0.834）。
数据结论
基于 MRI 的放射组学模型可能对检测 MRI 不可见的 EC 和 MI 具有良好的性能。
证据水平
3 级技术功效：2 级。
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