一种新的诺莫图谱和递归分区分析,用于预测皮下雷奥米奥萨科马患者的癌症特异性生存率
Qiang Ji1, Hua Hu2, Shulian Li3
1Department of Aesthetic Plastic Surgery, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, China.
Scientific reports
|February 4, 2024
概括
这项研究开发了一种名目录和风险分类系统,用于预测皮下乳腺瘤 (LMS) 患者的癌症特异性存活率 (CSS). 这些模型准确地识别高风险个体,帮助临床决策LMS.
科学领域:
- 在瘤学瘤学.
- 医学统计 医学统计
- 手术瘤学手术瘤学
背景情况:
- 准确预测皮下乳腺神经瘤 (LMS) 的预后,对于有效的治疗计划至关重要.
- 皮下LMS是一种罕见的软组织肉瘤,具有可变的临床行为.
研究的目的:
- 在被诊断患有皮下LMS的患者中开发和验证癌症特异性生存率 (CSS) 的预测模型.
- 在这个患者群体中确定影响CSS的独立风险因素.
主要方法:
- 对1793例皮下LMS病例的回顾性分析,随机分为培训和验证队列.
- 利用单变量和考克斯多变量回归,名录构造和递归分区分析用于模型开发.
- 使用C指数,校准图和CSS预测曲线下的面积 (AUC) 评估模型性能.
主要成果:
- 确定年龄,总结阶段,等级和手术作为皮下LMS的独立风险因素.
- 远程转移显示了最高的风险预测指标 (HR 4.325).
- 开发了一种C指数为0.802的诺米克图,并使用从0.803到0.859的AUC值验证了其预测准确性.
- 建立了一个风险分类系统,将患者分为四个分组,具有不同的CSS概率.
结论:
- 为皮下LMS患者开发了一种新型名谱和风险分类系统.
- 这些工具可以帮助临床医生识别高风险患者,并指导临床决策,以获得更好的患者结果.
- 这些模型在预测皮下LMS的癌症特异性生存率方面表现良好.
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