Wei Zhi-Yao, Zhang Zhe, Zhao Dong-Li, Zhao Wen-Ming, Meng Yuan-Guang
Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100700, China.
National Genomics Data Center and Chinese Academy of Sciences Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100700, China.
World J Clin Cases. 2024 Sep 16;12(26):5908-5921. doi: 10.12998/wjcc.v12.i26.5908.
Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC.
To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.
The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators.
Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature.
The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.
术前风险分层对子宫内膜癌（EC）患者的管理具有重要意义。基于磁共振成像（MRI）结合临床特征的放射组学可能有助于预测EC的风险等级。
构建机器学习模型，以基于从MRI中提取的放射组学特征预测EC患者的术前风险分层。
该研究纳入了112例EC患者。参与者按7:3的比例随机分为训练组和验证组。应用逻辑回归分析以揭示独立的临床预测因素。然后使用这些预测因素创建临床列线图。从MRI图像的T2加权成像和扩散加权成像序列中提取放射组学特征，采用曼-惠特尼检验、皮尔逊检验和最小绝对收缩和选择算子分析来评估相关的放射组学特征，随后将其用于生成放射组学特征标签。使用七种机器学习策略构建依赖于筛选特征的放射组学模型。采用逻辑回归方法构建一个综合列线图，该列线图纳入了放射组学特征标签和临床独立风险指标。
基于放射组学特征训练的随机森林方法表现优于预期，准确率为0.82，曲线下面积（AUC）为0.915 [95%置信区间（CI）：0.806-0.986]。放射组学预测模型的预测准确性超过了临床列线图（AUC：0.75，95%CI：0.611-0.899）和整合临床参数与放射组学特征标签的联合列线图（AUC：0.869，95%CI：0.702-0.986）。
基于MRI的放射组学模型可能是预测EC患者术前风险等级的有效工具。
