Song Zixuan, Wang Yizi, Zhang Dandan, Zhou Yangzi
Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
Front Oncol. 2020 Nov 26;10:608548. doi: 10.3389/fonc.2020.608548. eCollection 2020.
Uterine sarcoma is a rare gynecologic tumor with a high degree of malignancy. There is a lack of effective prognostic tools to predict early death of uterine sarcoma.
Data on patients with uterine sarcoma registered between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) data. Important independent prognostic factors were identified by univariate and multivariate logistic regression analyses to construct a nomogram for total early deaths and cancer-specific early deaths.
A total of 5,274 patients with uterine sarcoma were included in this study. Of which, 397 patients experienced early death (≤3 months), and 356 of whom died from cancer-specific causes. A nomogram for total early deaths and cancer-specific early deaths was created using data on age, race, tumor size, the International Federation of Gynecology and Obstetrics (FIGO) staging, histological classification, histological staging, treatment (surgery, radiotherapy, chemotherapy), and brain metastases. On comparing the C-index, area under the curve, and decision curve analysis, the created nomogram showed better predictive power and clinical practicality than one made exclusively with FIGO staging. Calibration of the nomogram by internal validation showed good consistency between the predicted and actual early death.
Nomograms that include clinical characteristics can provide a better prediction of the risk of early death for uterine sarcoma patients than nomograms only comprising the FIGO stage system. In doing so, this tool can help in identifying patients at high risk for early death because of uterine sarcoma.
子宫肉瘤是一种罕见的妇科肿瘤，恶性程度高。目前缺乏有效的预后工具来预测子宫肉瘤患者的早期死亡情况。
从监测、流行病学和最终结果（SEER）数据库中提取2004年至2015年间登记的子宫肉瘤患者数据。通过单因素和多因素逻辑回归分析确定重要的独立预后因素，以构建全因早期死亡和癌症特异性早期死亡的列线图。
本研究共纳入5274例子宫肉瘤患者。其中，397例患者经历了早期死亡（≤3个月），其中356例死于癌症相关原因。利用年龄、种族、肿瘤大小、国际妇产科联盟（FIGO）分期、组织学分类、组织学分级、治疗（手术、放疗、化疗）和脑转移等数据，创建了全因早期死亡和癌症特异性早期死亡的列线图。通过比较C指数、曲线下面积和决策曲线分析，发现所创建的列线图比仅基于FIGO分期的列线图具有更好的预测能力和临床实用性。通过内部验证对列线图进行校准，结果显示预测的早期死亡与实际早期死亡之间具有良好的一致性。
包含临床特征的列线图比仅包含FIGO分期系统的列线图能更好地预测子宫肉瘤患者的早期死亡风险。通过这种方式，该工具有助于识别因子宫肉瘤而有早期死亡高风险的患者。
