Objective: Predict the presence of lymphovascular space invasion (LVSI), using uterine factors such as tumor diameter (TD), grade, and depth of myometrial invasion (MMI). Develop a predictive model that could serve as a marker of LVSI in women with endometrial cancer (EC)./nMethods: Data from 888 patients with endometrioid EC who were treated between January 2009 and December 2018 were reviewed. The patients' data were retrieved from six institutions. We assessed the differences in the clinicopathological characteristics between patients with and without LVSI. We performed logistic regression analysis to determine which clinicopathological characteristics were the risk factors for positive LVSI status and to estimate the odds ratio (OR) for each covariate. Using the risk factors and OR identified through this process, we created a model that could predict LVSI and analyzed it further using receiver operating characteristic curve analysis./nResults: In multivariate logistic regression analysis, tumor size (P = 0.027), percentage of MMI (P < 0.001), and presence of cervical stromal invasion (P = 0.002) were identified as the risk factors for LVSI. Based on the results of multivariate logistic regression analysis, we developed a simplified LVSI prediction model for clinical use. We defined the “LVSI index” as “TD×%MMI×tumor grade×cervical stromal involvement.” The area under curve was 0.839 (95% CI= 0.809-0.869; sensitivity, 74.1%; specificity, 80.5%; negative predictive value, 47.3%; positive predictive value, 8.6%; P < 0.001), and the optimal cut-off value was 200./nConclusion: Using the modified risk index of LVSI, it is possible to predict the presence of LVSI in women with endometrioid endometrial cancer. Our prediction model may be an appropriate tool for integration into the clinical decision-making process when assessed either preoperatively or intraoperatively.

中文翻译：

---

子宫内膜癌患者淋巴血管间隙浸润的预测

目的：使用子宫因素，如肿瘤直径 (TD)、等级和子宫肌层浸润深度 (MMI)，预测淋巴血管间隙浸润 (LVSI) 的存在。开发可作为子宫内膜癌 (EC) 女性 LVSI 标志物的预测模型。/n方法：回顾了 2009 年 1 月至 2018 年 12 月期间接受治疗的 888 名子宫内膜样 EC 患者的数据。从六个机构检索患者的数据。我们评估了有和没有 LVSI 的患者临床病理特征的差异。我们进行逻辑回归分析以确定哪些临床病理特征是阳性 LVSI 状态的危险因素，并估计每个协变量的优势比 (OR)。使用通过此过程确定的风险因素和 OR，我们创建了一个可以预测 LVSI 的模型，并使用接收者操作特征曲线分析对其进行进一步分析。/n结果：在多变量逻辑回归分析中，肿瘤大小（P = 0.027）、MMI 百分比（P < 0.001）和宫颈间质浸润（P = 0.002）被确定为 LVSI 的危险因素。基于多变量逻辑回归分析的结果，我们开发了一个用于临床的简化 LVSI 预测模型。我们将“LVSI 指数”定义为“TD×%MMI×肿瘤分级×宫颈间质受累”。曲线下面积为 0.839（95% CI = 0.809-0.869；敏感性，74.1%；特异性，80.5%；阴性预测值，47.3%；阳性预测值，8.6%；P < 0.001），以及最佳临界值值为 200./n结论：使用改良的 LVSI 风险指数，可以预测子宫内膜样子宫内膜癌女性是否存在 LVSI。在术前或术中评估时，我们的预测模型可能是整合到临床决策过程中的合适工具。