PO.CL11.02 · 临床研究
生物电阻抗分析衍生的身体成分指标作为肺癌的预后生物标志物
Bioelectrical impedance analysis-derived body composition metrics as prognostic biomarkers in lung cancer
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：身体成分变化在肺癌中很常见，并与不良结局相关，但其治疗相关的变化和预后价值尚未得到很好界定。本研究对接受姑息治疗的肺癌患者的身体成分指标进行了连续评估。
方法：在本研究中，我们使用生物电阻抗分析仪（InBody 770和S10，InBody公司，韩国首尔）对接受姑息治疗的肺癌患者的身体成分指标进行了前瞻性连续评估。患者计划接受至少12周的治疗，除采集临床数据外，每3周进行一次分析。基于曲线下面积（AUC）和F1评分确定死亡和进展预测模型的最佳阈值。
结果：本研究共纳入141名患者。中位随访时间为21.3个月（范围13.2–24.9个月）。近半数队列年龄≥65岁（N=75，53.2%）。大多数患者为非小细胞肺癌（N=127，90.1%），较小一部分为小细胞肺癌（N=14，9.9%）。在纳入的患者中，92名（65.2%）接受了姑息性细胞毒性化疗（CTx组），49名（34.8%）接受了姑息性酪氨酸激酶抑制剂治疗（TKI组）。从基线到治疗结束，CTx组的相位角下降（从4.7±0.78降至4.48±0.71；p<0.001），细胞外水（ECW）比值升高（从0.392±0.008升至0.395±0.008；p<0.001）。而TKI组未观察到指标的显著变化。在使用特定时间点身体成分指标进行死亡预测方面，CTx组对1年内发生的事件表现出最高的效能，而TKI组对2年内的事件表现最佳。在进展预测方面，CTx组对6个月事件表现最佳，而TKI组对18个月事件表现出最佳效能。在CTx组中，预测模型对死亡的AUC为0.899，对进展的AUC为0.831，相应的F1评分分别为0.708和0.790。在TKI组中，死亡的AUC为0.961，进展的AUC为0.829，F1评分分别为0.640和0.817。Shapley加性解释（SHAP）分析将体脂百分比（PBF）和骨骼肌指数（SMI）确定为两种结局最具影响力的预测因素。
结论：连续的身体成分指标评估捕捉到了肺癌中治疗相关的身体成分变化，基于这些指标的模型展现出预后效能。这些发现支持在个性化患者管理中使用纵向身体成分监测。
查看英文原文 English abstract
Background: Body composition changes are common in lung cancer and relate to poor outcomes, but their treatment-related changes and prognostic value are not well defined. This study serially evaluated body composition metrics in patients with lung cancer receiving palliative treatment.
Methods: In this study, we prospectively and serially evaluated body composition metrics in patients with lung cancer receiving palliative treatment, using bioelectrical impedance analyzer (InBody 770 and S10, InBody Co., Seoul, Korea). The patients were scheduled for a minimum of 12 weeks of treatment, and the analysis was conducted every 3 weeks, in addition to collecting clinical data. The optimal thresholds of the death and progression prediction model were identified based on area under curve (AUC) and F1 score.
Results: A total of 141 patients were enrolled in the study. The median follow-up duration was 21.3 months (range, 13.2-24.9 months). Nearly half of the cohort was aged ≥65 years (N=75, 53.2%). The majority of patients had non-small cell lung cancer (N=127, 90.1%), with a smaller subset having small cell lung cancer (N=14, 9.9%). Among the enrolled patients, 92 (65.2%) received palliative cytotoxic chemotherapy (CTx group), and 49 (34.8%) received palliative tyrosine kinase inhibitor therapy (TKI group). From baseline to the end of treatment, CTx group showed a decrease in phase angle (from 4.7 ± 0.78 to 4.48 ± 0.71; p < 0.001) and an increase in extracellular water (ECW) ratio (from 0.392 ± 0.008 to 0.395 ± 0.008; p < 0.001). Whereas no significant changes of metrics were observed in TKI group. For death prediction using time-point-specific body composition metrics, the CTx group showed the highest performance for events occurring within 1 year, whereas the TKI group performed best for events within 2 years. For progression prediction, the CTx group performed best for 6-month events, while the TKI group showed the best performance for 18-month events. In the CTx group, the prediction models achieved AUCs of 0.899 for death and 0.831 for progression, with corresponding F1 scores of 0.708 and 0.790. In the TKI group, the AUCs were 0.961 for death and 0.829 for progression, with F1 scores of 0.640 and 0.817, respectively. Shapley Additive exPlanations (SHAP) analysis identified percent body fat (PBF) and skeletal muscle index (SMI) as the most influential predictors for both outcomes.
Conclusions: Serial body composition metrics assessments captured treatment-related body composition changes in lung cancer, and models based on these metrics showed prognostic performance. These findings support the use of longitudinal body composition monitoring in personalized patient management.
利益披露 Disclosure
J. Cha, None..
Y. Bang, None..
N. Kim, None..
J. Jung, None..
H. Kim, None..
J. Kim, None..
S. Cho, None..
A. Jin, None..
H. Kim, None..
S. Park, None.