Table_1_A Visualized Dynamic Prediction Model for Lymphatic Metastasis in Ewing's Sarcoma for Smart Medical Services.XLSX

BackgroundThis study aims to predict the lymphatic metastasis in Ewing's sarcoma (ES) patients by nomogram. The risk of lymphatic metastasis in patients with ES was predicted by the built model, which provided guidance for the clinical diagnosis and treatment planning.
MethodsA total of 929 patients diagnosed with ES were enrolled from the year of 2010 to 2016 in the Surveillance, Epidemiology, and End Results (SEER) database. The nomogram was established to determine predictive factors of lymphatic metastasis according to univariate and multivariate logistic regression analysis. The validation of the model performed using multicenter data (n = 51). Receiver operating characteristics (ROC) curves and calibration plots were used to evaluate the prediction accuracy of the nomogram. Decision curve analysis (DCA) was implemented to illustrate the practicability of the nomogram clinical application. Based on the nomogram, we established a web calculator to visualize the risk of lymphatic metastases. We further plotted Kaplan-Meier overall survival (OS) curves to compare the survival time of patients with and without lymphatic metastasis.
ResultsIn this study, the nomogram was established based on six significant factors (survival time, race, T stage, M stage, surgery, and lung metastasis), which were identified for lymphatic metastasis in ES patients. The model showed significant diagnostic accuracy with the value of the area under the curve (AUC) was 0.743 (95%CI: 0.714–0.771) for SEER internal validation and 0.763 (95%CI: 0.623–0.871) for multicenter data external validation. The calibration plot and DCA indicated that the model had vital clinical application value.
ConclusionIn this study, we constructed and developed a nomogram with risk factors to predict lymphatic metastasis in ES patients and validated accuracy of itself. We found T stage (Tx OR = 2.540, 95%CI = 1.433–4.503, P < 0.01), M stage (M1, OR = 2.061, 95%CI = 1.189–3.573, P < 0.05) and survival time (OR = 0.982, 95%CI = 0.972–0.992, P < 0.001) were important independent factors for lymphatic metastasis in ES patients. Furthermore, survival time in patients with lymphatic metastasis or unclear situation (P < 0.0001) was significantly lower. It can help clinicians make better decisions to provide more accurate prognosis and treatment for ES patients.

研究背景 本研究旨在通过列线图（nomogram）预测尤因肉瘤（Ewing's sarcoma, ES）患者的淋巴转移风险。所构建的模型可对ES患者的淋巴转移风险进行预测，为临床诊疗方案的制定提供指导。
研究方法 本研究从监测、流行病学与最终结果（Surveillance, Epidemiology, and End Results, SEER）数据库中纳入2010至2016年确诊的929例ES患者。通过单因素及多因素logistic回归分析，构建列线图以明确淋巴转移的预测因素。采用多中心数据（n=51）对模型进行验证。通过受试者工作特征（Receiver operating characteristics, ROC）曲线与校准曲线评估列线图的预测精度，借助决策曲线分析（Decision curve analysis, DCA）阐明该列线图的临床应用实用性。基于该列线图，我们搭建了在线计算器以可视化展示淋巴转移风险。此外，我们绘制了Kaplan-Meier总生存期（overall survival, OS）曲线，对比存在与不存在淋巴转移患者的生存时长差异。
研究结果 本研究基于6项与ES患者淋巴转移相关的显著因素构建列线图，分别为生存时间、种族、T分期、M分期、手术情况与肺转移。模型的诊断精度优异：SEER数据库内部验证的曲线下面积（area under the curve, AUC）为0.743（95%CI：0.714–0.771），多中心数据外部验证的AUC为0.763（95%CI：0.623–0.871）。校准曲线与决策曲线分析结果显示，该模型具备重要的临床应用价值。
研究结论 本研究构建并验证了一款基于风险因素的列线图，用于预测ES患者的淋巴转移风险，并验证了该模型的预测精度。研究发现，T分期（Tx OR=2.540，95%CI=1.433–4.503，P<0.01）、M分期（M1 OR=2.061，95%CI=1.189–3.573，P<0.05）与生存时间（OR=0.982，95%CI=0.972–0.992，P<0.001）是ES患者淋巴转移的重要独立影响因素。此外，存在淋巴转移或淋巴转移情况不明的患者，其生存时间显著更短（P<0.0001）。该列线图可帮助临床医师制定更合理的决策，为ES患者提供更精准的预后评估与治疗方案。