准确的预后评估对于子宫内膜癌（EC）的个体化治疗至关重要。尽管放射组学模型已证明 EC 的预后潜力，但感兴趣区域 (ROI) 描绘策略的影响和瘤周特征的临床意义仍不确定。因此，我们的研究旨在探讨不同放射组学模型对 EC 中 LVSI、DMI 和疾病分期的预测性能。对 174 例经组织病理学证实的 EC 患者进行回顾性分析。使用 2D 和 3D 方法在 T2 加权 MRI 图像上手动描绘 ROI。开发了六种放射组学模型，涉及瘤内（2Dintra 和 3Dintra）、瘤周（2Dperi 和 3Dperi）以及组合模型（2Dintra  peri 和 3Dintra  peri）。使用逻辑回归方法和五倍交叉验证构建模型。评估受试者工作特征曲线下面积（AUC），并使用 Delong 检验进行比较。在所有预测任务中，2Dintra 和 3Dintra 模型、2Dperi 和 3Dperi 模型的 AUC 均未观察到显着差异（P > 0.05） 。 3Dintra 和 3Dperi 模型的 LVSI（0.738 vs. 0.805）和 DMI 预测（0.719 vs. 0.804）之间观察到显着差异。在训练和验证队列中，与 3Dintra 模型相比，3Dintra  peri 模型在所有 3 个预测任务中都表现出明显更好的预测性能 (P < 0.05)。在 2D 和 3D 模型之间观察到了可比的预测性能。组合模型显着提高了预测性能，尤其是 3D 描绘，表明瘤内和瘤周特征可以为 EC 的综合预测提供补充信息。© 2024。作者。

Accurate prognostic assessment is vital for the personalized treatment of endometrial cancer (EC). Although radiomics models have demonstrated prognostic potential in EC, the impact of region of interest (ROI) delineation strategies and the clinical significance of peritumoral features remain uncertain. Our study thereby aimed to explore the predictive performance of varying radiomics models for the prediction of LVSI, DMI, and disease stage in EC.Patients with 174 histopathology-confirmed EC were retrospectively reviewed. ROIs were manually delineated using the 2D and 3D approach on T2-weighted MRI images. Six radiomics models involving intratumoral (2Dintra and 3Dintra), peritumoral (2Dperi and 3Dperi), and combined models (2Dintra + peri and 3Dintra + peri) were developed. Models were constructed using the logistic regression method with five-fold cross-validation. Area under the receiver operating characteristic curve (AUC) was assessed, and was compared using the Delong's test.No significant differences in AUC were observed between the 2Dintra and 3Dintra models, or the 2Dperi and 3Dperi models in all prediction tasks (P > 0.05). Significant difference was observed between the 3Dintra and 3Dperi models for LVSI (0.738 vs. 0.805) and DMI prediction (0.719 vs. 0.804). The 3Dintra + peri models demonstrated significantly better predictive performance in all 3 prediction tasks compared to the 3Dintra model in both the training and validation cohorts (P < 0.05).Comparable predictive performance was observed between the 2D and 3D models. Combined models significantly improved predictive performance, especially with 3D delineation, suggesting that intra- and peritumoral features can provide complementary information for comprehensive prognostication of EC.© 2024. The Author(s).