Department of General Surgery, Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, China.
State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, China.
Front Endocrinol (Lausanne). 2023 Jul 17;14:1160817. doi: 10.3389/fendo.2023.1160817. eCollection 2023.
Surgery is the best way to cure the retroperitoneal leiomyosarcoma (RLMS), and there is currently no prediction model on RLMS after surgical resection. The objective of this study was to develop a nomogram to predict the overall survival (OS) of patients with RLMS after surgical resection.
Patients who underwent surgical resection from September 2010 to December 2020 were included. The nomogram was constructed based on the COX regression model, and the discrimination was assessed using the concordance index. The predicted OS and actual OS were evaluated with the assistance of calibration plots.
118 patients were included. The median OS for all patients was 47.8 (95% confidence interval (CI), 35.9-59.7) months. Most tumor were completely resected (n=106, 89.8%). The proportions of French National Federation of Comprehensive Cancer Centres (FNCLCC) classification were equal as grade 1, grade 2, and grade 3 (31.4%, 30.5%, and 38.1%, respectively). The tumor diameter of 73.7% (n=85) patients was greater than 5 cm, the lesions of 23.7% (n=28) were multifocal, and 55.1% (n=65) patients had more than one organ resected. The OS nomogram was constructed based on the number of resected organs, tumor diameter, FNCLCC grade, and multifocal lesions. The concordance index of the nomogram was 0.779 (95% CI, 0.659-0.898), the predicted OS and actual OS were in good fitness in calibration curves.
The nomogram prediction model established in this study is helpful for postoperative consultation and the selection of patients for clinical trial enrollment.
手术是治疗腹膜后平滑肌肉瘤（RLMS）的最佳方法，目前尚无 RLMS 手术后的预测模型。本研究旨在建立一个列线图来预测 RLMS 患者手术后的总生存期（OS）。
纳入 2010 年 9 月至 2020 年 12 月期间接受手术切除的患者。基于 COX 回归模型构建列线图，并通过一致性指数评估判别能力。借助校准图评估预测 OS 和实际 OS。
共纳入 118 例患者。所有患者的中位 OS 为 47.8（95%置信区间（CI），35.9-59.7）个月。大多数肿瘤均完全切除（n=106，89.8%）。FNCLCC 分级的比例分别为 1 级（31.4%）、2 级（30.5%）和 3 级（38.1%）。73.7%（n=85）患者的肿瘤直径大于 5cm，23.7%（n=28）患者的病变为多灶性，55.1%（n=65）患者有一个以上器官被切除。列线图基于切除器官数量、肿瘤直径、FNCLCC 分级和多灶性病变构建。列线图的一致性指数为 0.779（95%CI，0.659-0.898），校准曲线显示预测 OS 和实际 OS 拟合良好。
本研究建立的列线图预测模型有助于术后咨询和临床试验入组患者的选择。
