Background: Current prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC patients. Methods: Contrast-Enhanced CT (CE-CT) scans from 81 EC cases were used to extract the radiomic features from semi-automatically contoured volumes of interest. We employed a 10-fold cross-validation approach with a 6:4 training to test set and utilized data augmentation and balancing techniques. Univariate analysis was applied for feature reduction leading to the development of three distinct machine learning (ML) models for the prediction of DFS: LASSO-Cox, CoxBoost and Random Forest (RFsrc). Results: In the training set, the ML models demonstrated AUCs ranging from 0.92 to 0.93, sensitivities from 0.96 to 1.00 and specificities from 0.77 to 0.89. In the test set, AUCs ranged from 0.86 to 0.90, sensitivities from 0.89 to 1.00 and specificities from 0.73 to 0.90. Patients classified as having a high recurrence risk prediction by ML models exhibited significantly worse DSF (p-value < 0.001) across all models. Conclusions: Our findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes.
背景：现有预后模型尚未利用术前CT图像预测子宫内膜癌（EC）患者的复发风险。本研究旨在探讨基于术前CT影像提取的放射组学特征对EC患者无病生存期（DFS）的预测潜力。方法：研究纳入81例EC患者的增强CT影像，通过半自动勾画感兴趣区域提取放射组学特征。采用10折交叉验证法按6:4比例划分训练集与测试集，并应用数据增强与平衡技术。通过单变量分析进行特征降维，进而构建三种机器学习模型（LASSO-Cox、CoxBoost和随机森林）用于DFS预测。结果：在训练集中，机器学习模型的曲线下面积（AUC）为0.92-0.93，敏感度0.96-1.00，特异度0.77-0.89；在测试集中，AUC为0.86-0.90，敏感度0.89-1.00，特异度0.73-0.90。所有模型均显示，被归类为高复发风险的患者其DFS显著更差（p值<0.001）。结论：本研究证实了放射组学在预测EC复发方面的应用潜力。尽管仍需进一步验证，但我们的研究结果凸显了放射组学在预测EC预后方面的广阔前景。
肿瘤(癌症)患者之家