Li Ling, Tao Weili, Ouyang Ze
Department of Medical Ultrasonic, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
Department of Medical Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Oncol. 2025 Sep 10;15:1609721. doi: 10.3389/fonc.2025.1609721. eCollection 2025.
This study aimed to develop and validate a prognostic nomogram to identify uterine sarcoma (US) patients who may not require adjuvant therapy after surgery, based on data from the Surveillance, Epidemiology, and End Results (SEER) database and an external Asian cohort.
Data from eligible uterine sarcoma patients in the USA ( = 1,626) who met the criteria of this study were collected from the SEER database and randomly divided into a training cohort ( = 1,138) and an internal validation cohort ( = 488). Multivariate Cox regression, Lasso regression, and crossvalidation were performed to select the optimal variables associated with survival. A nomogram-based model was then constructed to stratify the recurrence risk thresholds for the assessed patients. An external dataset from a separate cohort at our hospital ( = 90) was used to validate the accuracy and specificity of the nomogram model in discriminating patient risks, utilizing the consistency index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
Using the aforementioned classification aggregation methods, analysis of the training cohort identified diagnostic age, Fédération Internationale de Gynécologie et d'Obstétrique (FIGO) stage, grade, tumor size, and peritoneal cytology as independent predictors of overall survival (OS). The subsequent risk model demonstrated that patients with a threshold below 55 had a 10-year survival rate exceeding 80%, suggesting they may not require postoperative adjuvant therapy. Internal validation confirmed the reliability of this multiparameter model, as evidenced by a C-index of 0.77 and ROC AUC values of 0.812, 0.824, and 0.839 for 1-, 3-, and 5-year OS, respectively. Similarly, accuracy and specificity were confirmed by the external validation cohort, with a C-index exceeding 0.83, reaching a peak of 0.9, and ROC AUC values greater than 0.876. These results highlight that the stratified thresholds displayed by our nomogram outperformed FIGO staging in identifying low-risk recurrence patients.
Our constructed multiparameter nomogram model appears to be superior to the FIGO staging system in identifying low-risk patients who do not require adjuvant therapy after surgery, although prospective data are required for further validation.
本研究旨在基于监测、流行病学和最终结果（SEER）数据库及一个外部亚洲队列的数据，开发并验证一种预后列线图，以识别术后可能不需要辅助治疗的子宫肉瘤（US）患者。
从SEER数据库中收集符合本研究标准的美国合格子宫肉瘤患者（n = 1626）的数据，并随机分为训练队列（n = 1138）和内部验证队列（n = 488）。进行多变量Cox回归、Lasso回归和交叉验证，以选择与生存相关的最佳变量。然后构建基于列线图的模型，对评估患者的复发风险阈值进行分层。利用一致性指数（C-index）、受试者操作特征（ROC）曲线、校准曲线和决策曲线分析（DCA），使用我院一个单独队列的外部数据集（n = 90）来验证列线图模型在区分患者风险方面的准确性和特异性。
使用上述分类汇总方法，对训练队列的分析确定诊断年龄、国际妇产科联合会（FIGO）分期、分级、肿瘤大小和腹膜细胞学为总生存（OS）的独立预测因素。随后的风险模型表明，阈值低于55的患者10年生存率超过80%，表明他们可能不需要术后辅助治疗。内部验证证实了该多参数模型的可靠性，1年、3年和5年OS的C-index分别为0.77，ROC AUC值分别为0.812、0.824和0.839。同样，外部验证队列证实了准确性和特异性，C-index超过0.83，峰值达到0.9，ROC AUC值大于0.876。这些结果表明，我们的列线图显示的分层阈值在识别低风险复发患者方面优于FIGO分期。
我们构建的多参数列线图模型在识别术后不需要辅助治疗的低风险患者方面似乎优于FIGO分期系统，尽管需要前瞻性数据进行进一步验证。
