背景:软骨肉瘤(CHS),骨恶性肿瘤,由于其异质性和对常规治疗的抗性,提出了重大挑战。显然需要先进的预后工具,可以整合多个预后因素,为个体患者提供个性化的生存预测。本研究旨在开发一种基于递归分区分析(RPA)的新型预测工具,以提高CHS患者的总体生存率。
方法:来自监测的数据,流行病学,和最终结果(SEER)数据库进行了分析,包括人口统计,临床,以及2000年至2018年间诊断的患者的治疗细节。使用C5.0算法,创建决策树来预测12、24、60和120个月的生存概率。通过混淆散点图评估模型的性能,准确率,接收器操作员特征(ROC)曲线,ROC曲线下面积(AUC)。
结果:该研究确定了肿瘤组织学,手术,年龄,内脏(脑/肝/肺)转移,化疗,肿瘤分级,和性别作为关键的预测因素。决策树在每个时间点显示了不同的生存预测模式。模型显示出较高的准确性(训练组为82.40%-89.09%,试验组82.16%-88.74%)和辨别力(训练组AUC:0.806-0.894,测试组中的0.808-0.882)在训练和测试数据集中。基于Web的交互式闪亮APP(URL:https://yangxg1209。shinyapps.io/软骨肉瘤_生存_预测/)被开发,简化临床医生的生存预测过程。
结论:这项研究成功地使用RPA开发了一种用户友好的工具,用于CHS的个性化生存预测。决策树模型展示了强大的预测能力,与交互式应用程序促进临床决策。建议未来的前瞻性研究来验证这些发现并进一步完善预测模型。
BACKGROUND: Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS.
METHODS: Data from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC).
RESULTS: The study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%-89.09% in training group, and 82.16%-88.74% in test group) and discriminatory power (AUC: 0.806-0.894 in training group, and 0.808-0.882 in test group) in both training and testing datasets. An interactive web-based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma_survival_prediction/) was developed, simplifying the survival prediction process for clinicians.
CONCLUSIONS: This study successfully employed RPA to develop a user-friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision-making. Future prospective studies are recommended to validate these findings and further refine the predictive model.