Yuan Yufei, Wang Ruoran, Zhang Yidan, Yang Yang, Zhao Jing
Center of Reproductive Medicine, Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, China.
Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
Front Surg. 2022 Jul 15;9:855314. doi: 10.3389/fsurg.2022.855314. eCollection 2022.
Lung metastasis (LM) is an independent risk factor for survival in patients with endometrial cancer (EC).
We reviewed data on patients diagnosed with EC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. The independent predictors of LM in patients with EC were identified using univariate and multivariate logistic regression analyses. A nomogram for predicting LM in patients with EC was developed, and the predictive model was evaluated using calibration and receiver operating characteristic (ROC) curves.
Univariate and multivariate logistic regression analyses showed that high grade; specific histological type; high tumor and node stages; larger tumor size; and liver, brain, and bone metastases were positively associated with LM risk. A new nomogram was developed by combining these factors to predict LM in patients newly diagnosed with EC. Internal and external verification of the calibration charts showed that the nomogram was well calibrated. The areas under the ROC curves for the training and validation cohorts were 0.924 and 0.913, respectively.
We performed a retrospective analysis of 42,073 patients with EC using the SEER database, established a new nomogram for predicting LM based on eight independent risk factors, and visualized the model using a nomogram for the first time.
肺转移（LM）是子宫内膜癌（EC）患者生存的独立危险因素。
我们回顾了2010年至2015年期间来自监测、流行病学和最终结果（SEER）数据库中被诊断为EC的患者的数据。使用单因素和多因素逻辑回归分析确定EC患者发生LM的独立预测因素。建立了预测EC患者发生LM的列线图，并使用校准曲线和受试者工作特征（ROC）曲线对预测模型进行评估。
单因素和多因素逻辑回归分析表明，高分级；特定组织学类型；高肿瘤和淋巴结分期；更大的肿瘤大小；以及肝、脑和骨转移与LM风险呈正相关。通过结合这些因素开发了一种新的列线图，以预测新诊断为EC的患者发生LM的情况。校准图的内部和外部验证表明列线图校准良好。训练队列和验证队列ROC曲线下面积分别为0.924和0.913。
我们使用SEER数据库对42073例EC患者进行了回顾性分析，基于八个独立危险因素建立了一种新的预测LM的列线图，并首次使用列线图将该模型可视化。
