Department of Orthopaedic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Medical College of Qingdao University, Qingdao, Shandong, China.
Technol Cancer Res Treat. 2021 Jan-Dec;20:1533033821997828. doi: 10.1177/1533033821997828.
The goal of this study is to construct nomograms to effectively predict the distant metastatic sites and overall survival (OS) of soft tissue sarcoma (STS) patients.
STS case data between 2010 and 2015 for retrospective study were gathered from public databases. According to the chi-square and multivariate logistic regression analysis determined independent predictive factors of specific metastatic sites, the nomograms based on these factors were consturced. Subsequently, combined metastatic information a nomogram to predict 1-, 2-, and 3-year OS of STS patients was developed. The performance of models was validated by the area under the curve (AUC), calibration plots, and decision curve analyses (DCA).
A total of 7001 STS patients were included in this retrospective study, including 4901 cases in the training group and the remaining 2,100 patients in the validation group. Three nomograms were established to predict lung, liver and bone metastasis, and satisfactory results have been obtained by internal and external validation. The AUCs for predicting lung, liver, and bone metastases in the training cohort were 0.796, 0.799, and 0.766, respectively, and in the validation cohort were 0.807, 0.787, and 0.775, respectively, which means that the nomograms have good discrimination. The calibration curves showed that the models have high precision, and the DCA manifested that the nomograms have great clinical application prospects. Through univariate and multivariate COX regression analyses, 8 independent prognosis factors of age, grade, histological type, tumor size, surgery, chemotherapy, radiatiotherapy and lung metastasis were determined. A nomogram was then constructed to predict the 1-, 2-, and 3-years OS, which has a good performance in both internal and external validations.
The nomograms for predicting specific metastatic sites and OS have good discrimination, accuracy and clinical applicability. The models could accurately predict the metastatic risk and survival information, and help clinical decision-making.
本研究旨在构建列线图，以有效预测软组织肉瘤（STS）患者的远处转移部位和总体生存（OS）。
本研究回顾性收集了 2010 年至 2015 年公共数据库中的 STS 病例数据。根据卡方检验和多因素逻辑回归分析确定特定转移部位的独立预测因素，基于这些因素构建列线图。随后，结合转移信息，构建了预测 STS 患者 1、2 和 3 年 OS 的列线图。通过曲线下面积（AUC）、校准图和决策曲线分析（DCA）验证模型的性能。
本研究共纳入 7001 例 STS 患者，其中训练组 4901 例，验证组 2100 例。建立了 3 个列线图预测肺、肝和骨转移，内部和外部验证均取得了满意的结果。训练队列中预测肺、肝和骨转移的 AUC 分别为 0.796、0.799 和 0.766，验证队列中分别为 0.807、0.787 和 0.775，表明列线图具有良好的区分度。校准曲线显示模型具有较高的精度，DCA 表明列线图具有很好的临床应用前景。通过单因素和多因素 COX 回归分析，确定了年龄、分级、组织学类型、肿瘤大小、手术、化疗、放疗和肺转移 8 个独立的预后因素。然后构建了一个列线图来预测 1、2 和 3 年的 OS，内部和外部验证均具有良好的性能。
预测特定转移部位和 OS 的列线图具有良好的区分度、准确性和临床适用性。该模型可以准确预测转移风险和生存信息，有助于临床决策。
