Huang Chao, Huang Zhangheng, Zhou Zongke
Department of Orthopedics, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu 610041, Sichuan, China.
J Oncol. 2022 Aug 27;2022:7831001. doi: 10.1155/2022/7831001. eCollection 2022.
Fibrosarcoma (FS) is a typically invasive sarcoma formed by fibroblasts and collagen fibers. Currently, the standard treatment for FS is the surgical resection, but the high recurrence rate and poor prognosis limit the benefits of postoperative patients. Exploring what factors affect the benefit of postoperative patients is significant for guiding the implementation of surgical resection. Therefore, this study aims to construct a novel nomogram to predict the cancer-specific survival (CSS) of postoperative fibrosarcoma (POFS) patients.
The included patients were randomly assigned to the training and validation sets at a ratio of 7 : 3. CSS was indexed as the research endpoint. Firstly, univariate and multivariate Cox regression analyses were used on the training set to determine independent prognostic predictors and build a nomogram for predicting the 1-, 3-, and 5-year CSS of POFS patients. Secondly, the nomogram's discriminative power and prediction accuracy were evaluated by receiver operating characteristic (ROC) and the calibration curve, and a risk classification system for POFS patients was constructed. Finally, the nomogram's clinical utility was evaluated using decision curve analysis (DCA).
Our study included 346 POFS patients, divided into the training (244) and validation sets (102). Multivariate Cox regression analysis demonstrated that tumor size, SEER stage, and tumor grade were independent prognostic predictors of CSS for POFS patients. They were used to create a nomogram. In the training and validation sets, the ROC curve showed that the 1-, 3-, and 5-year area under the curve (AUC) were higher than 0.700, indicating that the nomogram had good reliability and accuracy. DCA also showed that the nomogram has high application value in clinical practice.
The larger tumor size, higher tumor grade, and distant metastasis were independently related to the poor prognosis of POFS patients. The nomogram constructed based on the above variables could accurately predict the 1-, 3-, and 5-year CSS of POFS patients. So, the nomogram and risk classification system we built might help make accurate judgments in clinical practice, optimize patient treatment decisions, maximize postoperative benefits, and ultimately improve the prognosis of POFS patients.
纤维肉瘤（FS）是一种由成纤维细胞和胶原纤维构成的典型侵袭性肉瘤。目前，FS的标准治疗方法是手术切除，但高复发率和不良预后限制了术后患者的获益。探索哪些因素影响术后患者的获益对于指导手术切除的实施具有重要意义。因此，本研究旨在构建一种新型列线图，以预测纤维肉瘤术后（POFS）患者的癌症特异性生存（CSS）。
将纳入的患者按7∶3的比例随机分配到训练集和验证集。将CSS作为研究终点。首先，在训练集上进行单因素和多因素Cox回归分析，以确定独立的预后预测因素，并构建预测POFS患者1年、3年和5年CSS的列线图。其次，通过受试者工作特征（ROC）曲线和校准曲线评估列线图的辨别力和预测准确性，并构建POFS患者的风险分类系统。最后，使用决策曲线分析（DCA）评估列线图的临床实用性。
本研究纳入346例POFS患者，分为训练集（244例）和验证集（102例）。多因素Cox回归分析表明，肿瘤大小、SEER分期和肿瘤分级是POFS患者CSS的独立预后预测因素。利用这些因素创建了列线图。在训练集和验证集中，ROC曲线显示1年、3年和5年曲线下面积（AUC）均高于0.700，表明列线图具有良好的可靠性和准确性。DCA也表明列线图在临床实践中具有较高的应用价值。
肿瘤体积较大、肿瘤分级较高和远处转移与POFS患者的不良预后独立相关。基于上述变量构建的列线图能够准确预测POFS患者1年、3年和5年的CSS。因此，我们构建的列线图和风险分类系统可能有助于在临床实践中做出准确判断，优化患者治疗决策，使术后获益最大化，最终改善POFS患者的预后。
