有效地使用PRO来预测生存率:在NSCLC患者中基于变压器的建模
D Dudas1, T J Dilling2, H Jim3
1LMU Hospital, Department of Radiation Oncology, Munich, Germany; H. Lee Moffitt Cancer Center and Research Institute, Department of Machine Learning, Tampa, FL, USA.
概括
使用患者报告结果 (PROs) 的变压器模型改善了SBRT治疗的早期非小细胞肺癌 (NSCLC) 患者的生存预测准确度. 食欲丧失和疼痛是生存的最重要的预测因素.
科学领域:
- 在瘤学瘤学.
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
背景情况:
- 准确的生存预测对于以患者为中心的护理,治疗规划和瘤学中的息护理转诊至关重要.
- 目前的临床生存估计可能过于乐观,可能会降低患者的生活质量 (QoL).
- 患者报告的结果 (PROs) 是有价值的预测指标,可以提高预后准确性.
研究的目的:
- 探索变压器架构,利用纵向PRO轨迹来提高早期非小细胞肺癌 (NSCLC) 患者的生存预测准确度.
- 通过使用变压器模型,识别使用变压器模型对生存预测最具有预后相关的PRO症状.
主要方法:
- 开发了一个基于变压器的模型来分析475名接受SBRT治疗的早期NSCLC患者的纵向PRO数据 (埃德蒙顿症状评估量表 - ESAS).
- 该模型结合了PRO,临床和人口统计变量来预测整体生存期 (OS),将性能与Cox比例危险回归和联合概率模型进行比较.
- 以c指数和AUC为指导的SHapley添加剂扩张 (SHAP) 值和逆向淘汰被用于模型解释和症状识别.
主要成果:
- 变压器模型在发现组中实现了0.753的交叉验证c指数和0.862的AUC,在持有测试组中实现了0.694的c指数和0.785的AUC.
- 变压器模型显著优于传统的Cox和联合概率生存模型.
- SHAP分析发现食欲丧失,疼痛,整体幸福感和呼吸短促是预测生存的最重要的预后症状.
结论:
- 整合纵向PRO的基于变压器的生存模型显著提高了SBRT治疗NSCLC患者的预后准确性.
- 失去食欲和疼痛被确定为生存的最强有力的预测因素,其次是整体幸福感和呼吸短促.
- 针对性,以症状为重点的PRO跟踪可以提高生存估计,并简化在常规瘤治疗中的临床实施.
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