Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：手术是原发性腹膜后肉瘤（RPS）治疗的基石。目的建立预测原发性RPS术后发病率的列线图预测模型。
方法：回顾性分析2009-2021年行根治性切除术患者的临床病理资料。危险因素分析采用logistic回归模型,并根据Akaike信息准则选择建模变量。在二元逻辑回归模型的基础上建立了列线图预测模型，并通过校准曲线和协调指数进行了内部验证。
结果：共纳入319例患者，包括162名男性(50.8%)。22.9%(n=73)年龄在65岁以上，70.2%(n=224)的肿瘤大于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例（31.1％）和31例（11.6％）患者有I-II级并发症和III-V级并发症，分别。年龄,肿瘤负荷，location,手术时间,联合器官切除的数量，加权切除器官评分，估计失血量和充血红细胞输血用于构建列线图，其一致性指数为0.795(95%CI0.746-0.844)。校准曲线表明预测速率和实际速率之间有很高的一致性。
结论：列线图，一种综合多种临床病理因素的视觉预测工具，可以帮助医师筛查术后并发症高危RPS患者,为早期干预提供依据。
方法：回顾性分析2009-2021年行根治性切除术患者的临床病理资料。
结果：共纳入319例患者，
结论：列线图，
