Department of Radiation Oncology, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
INSERM, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France.
Radiother Oncol. 2020 May;146:44-51. doi: 10.1016/j.radonc.2020.02.002. Epub 2020 Feb 27.
The aim of this study was to identify subgroups of locally advanced NSCLC patients with a distinct treatment response during concurrent chemoradiotherapy (CCRT). Subsequently, we investigated the association of subgroup membership with treatment outcomes.
394 NSCLC-patients treated with CCRT between 2007 and 2013 were included. Gross Tumor Volume (GTV) during treatment was determined and relative GTV-volume change from the planning-CT was subsequently calculated. Latent Class Mixed Modeling (LCMM) was used to identify subgroups with distinct volume changes during CCRT. The association of subgroup membership with overall survival (OS), progression free survival (PFS) and local regional control (LRC) was assessed using cox regression analyses.
Three subgroups of GTV-volume change during treatment were identified, with each subsequent subgroup showing a more profound reduction of GTV during treatment. No associations between subgroup membership and OS, PFS nor LRC were observed. Nonetheless, baseline GTV (HR1.42; 95%CI 1.06-1.91) was significantly associated with OS.
Three different subgroups of GTV-volume change during treatment were identified. Surprisingly, these subgroups did not differ in their risk of treatment outcomes. Only patients with a larger GTV at baseline had a significantly worse OS. Therefore, risk stratification at baseline might already be accurate in identifying the best treatment strategy for most patients.
本研究旨在确定局部晚期 NSCLC 患者在同期放化疗(CCRT)期间具有不同治疗反应的亚组。随后,我们调查了亚组归属与治疗结果的相关性。
纳入了 2007 年至 2013 年间接受 CCRT 治疗的 394 名 NSCLC 患者。在治疗过程中确定了大体肿瘤体积(GTV),并随后计算了从计划 CT 计算得出的相对 GTV 体积变化。潜在类别混合模型(LCMM)用于识别 CCRT 期间具有明显体积变化的亚组。使用 Cox 回归分析评估亚组归属与总生存(OS)、无进展生存(PFS)和局部区域控制(LRC)的相关性。
确定了治疗过程中 GTV 体积变化的三个亚组,每个后续亚组在治疗过程中 GTV 的减少更为明显。未观察到亚组归属与 OS、PFS 或 LRC 之间存在关联。尽管如此,基线 GTV(HR1.42;95%CI 1.06-1.91)与 OS 显著相关。
确定了治疗过程中 GTV 体积变化的三个不同亚组。令人惊讶的是,这些亚组在治疗结果的风险方面没有差异。只有基线 GTV 较大的患者 OS 显著更差。因此,基线风险分层可能已经能够准确地确定大多数患者的最佳治疗策略。