The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing, China.
Cancer Med. 2023 Apr;12(8):9260-9271. doi: 10.1002/cam4.5668. Epub 2023 Mar 23.
Malignant myofibroblastic tumors are a rare group of soft tissue sarcomas, for which a prognosis prediction model is lacking. Based on the Surveillance, Epidemiology, and End Results (SEER) database and cases from Nanjing Drum Tower Hospital, the current study constructed and validated a nomogram to assess overall survival of patients with malignant myofibroblastic tumors.
Data of patients with myofibroblastic tumors diagnosed between 2000 and 2018 were extracted from the SEER database. Similarly, data of patients with myofibroblastic tumor in Nanjing Drum Tower Hospital between May 2016 and March 2022 were collected. Then, we conducted univariate and multivariate Cox analyses to identify independent prognostic parameters to develop the nomogram. The model was evaluated by concordance index (C-index), calibration curve, the area under the curve (AUC), decision curve analysis (DCA), Kaplan-Meier analysis, and subgroup analyses.
Seven variables were selected to construct the nomogram. The results of the C-index (0.783), calibration curve, the AUCs, and subgroup analyses demonstrated the accurate predictive capacity and excellent discriminative ability of the nomogram. The DCA of the model indicated its better clinical net benefit than that of the traditional system.
Evaluation of the predictive performance of the nomogram revealed the superior sensitivity and specificity of the model and the higher prediction accuracy of the outcomes compared with those of the traditional system. The established nomogram may assist patients in consultation and help physicians in clinical decision-making.
恶性肌纤维母细胞瘤是一种罕见的软组织肉瘤，目前缺乏预后预测模型。本研究基于监测、流行病学和最终结果（SEER）数据库以及南京鼓楼医院的病例，构建并验证了一个列线图，以评估恶性肌纤维母细胞瘤患者的总生存率。
从 SEER 数据库中提取 2000 年至 2018 年间诊断为肌纤维母细胞瘤患者的数据。同样，收集了 2016 年 5 月至 2022 年 3 月南京鼓楼医院肌纤维母细胞瘤患者的数据。然后，我们进行单因素和多因素 Cox 分析，以确定独立的预后参数来建立列线图。通过一致性指数（C 指数）、校准曲线、曲线下面积（AUC）、决策曲线分析（DCA）、Kaplan-Meier 分析和亚组分析来评估模型。
选择了 7 个变量来构建列线图。C 指数（0.783）、校准曲线、AUCs 和亚组分析的结果表明，该列线图具有准确的预测能力和良好的区分能力。该模型的 DCA 表明，其临床净获益优于传统系统。
对列线图预测性能的评估显示，该模型具有较高的敏感性和特异性，以及与传统系统相比更高的预测准确性。建立的列线图可能有助于患者咨询，并帮助医生进行临床决策。
