Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China.
Int J Environ Res Public Health. 2022 Nov 25;19(23):15654. doi: 10.3390/ijerph192315654.
This study assessed the predictive value of the metabolic risk score (MRS) for lymphovascular space invasion (LVSI) in endometrial cancer (EC) patients.
We included 1076 patients who were diagnosed with EC between January 2006 and December 2020 in Peking University People's Hospital. All patients were randomly divided into the training and validation cohorts in a ratio of 2:1. Data on clinicopathological indicators were collected. Univariable and multivariable logistic regression analysis was used to define candidate factors for LVSI. A backward stepwise selection was then used to select variables for inclusion in a nomogram. The performance of the nomogram was evaluated by discrimination, calibration, and clinical usefulness.
Independent predictors of LVSI included differentiation grades (G2: OR = 1.800, 95% CI: 1.050-3.070, = 0.032) (G3: OR = 3.49, 95% CI: 1.870-6.520, < 0.001), histology (OR = 2.723, 95% CI: 1.370-5.415, = 0.004), MI (OR = 4.286, 95% CI: 2.663-6.896, < 0.001), and MRS (OR = 1.124, 95% CI: 1.067-1.185, < 0.001) in the training cohort. A nomogram was established to predict a patient's probability of developing LVSI based on these factors. The ROC curve analysis showed that an MRS-based nomogram significantly improved the efficiency of diagnosing LVSI compared with the nomogram based on clinicopathological factors ( = 0.0376 and = 0.0386 in the training and validation cohort, respectively). Subsequently, the calibration plot showed a favorable consistency in both groups. Moreover, we conducted a decision curve analysis, showing the great clinical benefit obtained from the application of our nomogram. However, our study faced several limitations. Further external validation and a larger sample size are needed in future studies.
MRS-based nomograms are useful for predicting LVSI in patients with EC and may facilitate better clinical decision-making.
本研究评估了代谢风险评分（MRS）对子宫内膜癌（EC）患者淋巴管血管侵犯（LVSI）的预测价值。
我们纳入了 2006 年 1 月至 2020 年 12 月期间在北京大学人民医院诊断为 EC 的 1076 例患者。所有患者均按 2:1 的比例随机分为训练队列和验证队列。收集临床病理指标数据。采用单变量和多变量逻辑回归分析确定 LVSI 的候选因素。然后采用向后逐步选择法选择纳入列线图的变量。通过区分度、校准度和临床实用性评估列线图的性能。
LVSI 的独立预测因素包括分化程度（G2：OR=1.800，95%CI：1.050-3.070， = 0.032）（G3：OR=3.49，95%CI：1.870-6.520， < 0.001）、组织学类型（OR=2.723，95%CI：1.370-5.415， = 0.004）、肌层浸润（MI）（OR=4.286，95%CI：2.663-6.896， < 0.001）和 MRS（OR=1.124，95%CI：1.067-1.185， < 0.001）。在训练队列中，建立了一个基于这些因素预测患者发生 LVSI 概率的列线图。ROC 曲线分析显示，基于 MRS 的列线图显著提高了诊断 LVSI 的效率，优于基于临床病理因素的列线图（在训练和验证队列中的 = 0.0376 和 = 0.0386）。随后，校准图显示两组均具有良好的一致性。此外，我们进行了决策曲线分析，表明我们的列线图具有很大的临床获益。然而，本研究存在一些局限性。未来的研究需要进一步的外部验证和更大的样本量。
基于 MRS 的列线图可用于预测 EC 患者的 LVSI，有助于更好地进行临床决策。
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