Department of Neurology, Department of Medical Oncology, Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China.
Xiamen University Research Center of Retroperitoneal Tumor Committee of Oncology Society of Chinese Medical Association, Xiamen University, Xiamen, Fujian, China.
BMC Surg. 2023 Feb 23;23(1):42. doi: 10.1186/s12893-023-01941-8.
Surgery is the cornerstone of the treatment for primary retroperitoneal sarcoma (RPS). The purpose of this study was to establish a nomogram predictive model for predicting postoperative morbidity in primary RPS.
Clinicopathological data of patients who underwent radical resection from 2009 to 2021 were retrospectively analyzed. Risk factor analysis was performed using a logistic regression model, and modeling variables were selected based on Akaike Information Criterion. The nomogram prediction model was built on the basis of a binary logistic regression model and internally validated by calibration curves and concordance index.
A total of 319 patients were enrolled, including 162 males (50.8%). 22.9% (n = 73) were over 65 years of age, and 70.2% (n = 224) had tumors larger than 10 cm. The most common histologic subtypes were well-differentiated liposarcoma (38.2%), dedifferentiated liposarcoma (25.1%) and leiomyosarcoma (7.8%). According to the Clavien-Dindo Classification, 96 (31.1%) and 31 (11.6%) patients had grade I-II complications and grade III-V complications, respectively. Age, tumor burden, location, operative time, number of combined organ resections, weighted resected organ score, estimated blood loss and packed RBC transfusion was used to construct the nomogram, and the concordance index of which was 0.795 (95% CI 0.746-0.844). and the calibration curve indicated a high agreement between predicted and actual rates.
Nomogram, a visual predictive tool that integrates multiple clinicopathological factors, can help physicians screen RPS patients at high risk for postoperative complications and provide a basis for early intervention.
手术是原发性腹膜后肉瘤（RPS）治疗的基石。本研究旨在建立预测原发性 RPS 术后发病率的列线图预测模型。
回顾性分析 2009 年至 2021 年接受根治性切除术的患者的临床病理数据。使用逻辑回归模型进行风险因素分析，并根据赤池信息量准则选择建模变量。基于二项逻辑回归模型构建列线图预测模型，并通过校准曲线和一致性指数进行内部验证。
共纳入 319 例患者，其中男性 162 例（50.8%）。22.9%（n=73）患者年龄超过 65 岁，70.2%（n=224）肿瘤大于 10cm。最常见的组织学亚型是高分化脂肪肉瘤（38.2%）、去分化脂肪肉瘤（25.1%）和平滑肌肉瘤（7.8%）。根据 Clavien-Dindo 分类，96（31.1%）和 31（11.6%）例患者分别发生 I-II 级和 III-V 级并发症。年龄、肿瘤负担、位置、手术时间、联合器官切除数量、加权切除器官评分、估计失血量和红细胞悬液输注用于构建列线图，其一致性指数为 0.795（95%CI 0.746-0.844）。校准曲线表明预测率与实际率之间具有高度一致性。
列线图是一种整合多个临床病理因素的可视化预测工具，可以帮助医生筛选术后并发症风险较高的 RPS 患者，并为早期干预提供依据。
