手术是治疗腹腔后肌层间平滑肌肉瘤(RLMS)的最佳方式，目前尚无RLMS手术切除后的预测模型。本研究的目标是开发一种预测RLMS患者在手术切除后的总生存率(OS)的正态图。纳入了2010年9月至2020年12月接受手术切除的患者。正态图是基于COX回归模型构建的，利用一致性指数评估了判别能力。借助校准图评估了预测的OS和实际的OS。共纳入了118例患者。所有患者的中位OS为47.8个月(95%置信区间(CI)35.9-59.7)。大多数肿瘤被完全切除(n=106，占89.8%)。法国国家癌症中心联盟(FNCLCC)分类的比例为1级、2级和3级(分别为31.4%、30.5%和38.1%)。73.7%(n=85)的患者肿瘤直径大于5厘米，23.7%(n=28)的病灶为多发性，55.1%(n=65)的患者有一个以上的器官被切除。OS正态图是基于切除器官的数量、肿瘤直径、FNCLCC分级和多灶性病灶构建的。正态图的一致性指数为0.779(95%CI, 0.659-0.898)，预测的OS与实际的OS在校准曲线中良好适合。本研究建立的正态图预测模型对于术后咨询和临床试验入组患者的选择是有帮助的。版权所有©2023 Zhuang, Yue, Tong, Zhang, He and Lu.

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.Copyright © 2023 Zhuang, Yue, Tong, Zhang, He and Lu.