Department of Orthopedics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
Department of Neonatology, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
J Int Med Res. 2023 Jul;51(7):3000605231188647. doi: 10.1177/03000605231188647.
This study investigated risk factors and constructed an online tool to predict distant metastasis (DM) risk in patients with leiomyosarcoma (LMS) after surgical resection.
Data regarding patients with LMS who underwent surgical resection between 2010 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Data were collected regarding patients with LMS who underwent surgical resection at Tianjin Medical University Cancer Hospital and Institute (TJMUCH) between October 2010 and July 2018. Patients were randomly divided into training and validation sets. Logistic regression analyses were performed; a nomogram was established. The area under the curve (AUC) and calibration curve were used to evaluate the nomogram, which served as the basis for a web-based nomogram.
This study included 4461 and 76 patients from the SEER database and TJMUCH, respectively. Age, ethnicity, grade, T stage, N stage, radiotherapy, and chemotherapy were associated with DM incidence. C-index values were 0.815 and 0.782 in the SEER and Chinese datasets, respectively; corresponding AUC values were 0.814 and 0.773, respectively. A web-based nomogram (https://weijunqiang-leimyosarcoma-seer.shinyapps.io/dynnomapp/) was established.
Our web-based nomogram is an accurate and user-friendly tool to predict DM risk in patients with LMS; it can aid clinical decision-making.
本研究旨在探讨影响平滑肌肉瘤（LMS）患者术后远处转移（DM）的风险因素，并构建一个在线预测工具。
从监测、流行病学和最终结果（SEER）数据库中提取 2010 年至 2018 年间接受手术切除的 LMS 患者数据。同时，从 2010 年 10 月至 2018 年 7 月天津医科大学肿瘤医院和研究所（TJMUCH）接受手术切除的 LMS 患者中收集数据。患者被随机分为训练集和验证集。进行逻辑回归分析；建立一个列线图。通过曲线下面积（AUC）和校准曲线评估列线图，作为基于网络的列线图的基础。
本研究纳入了 SEER 数据库和 TJMUCH 的 4461 例和 76 例患者。年龄、种族、分级、T 分期、N 分期、放疗和化疗与 DM 发生率相关。SEER 和中国数据集的 C 指数值分别为 0.815 和 0.782；相应的 AUC 值分别为 0.814 和 0.773。建立了一个基于网络的列线图（https://weijunqiang-leimyosarcoma-seer.shinyapps.io/dynnomapp/）。
我们的基于网络的列线图是一种准确且易于使用的工具，可用于预测 LMS 患者的 DM 风险，有助于临床决策。
