Temporal evolution of CT imaging features in oligometastatic lung lesions after stereotactic body radiation therapy: a multicenter retrospective study of early tumor response as a predictor of favorable local control

立体定向放射治疗后寡转移性肺病灶CT影像特征的时间演变:一项多中心回顾性研究,探讨早期肿瘤反应作为预测局部控制良好疗效的指标

阅读:1

Abstract

BACKGROUND: Stereotactic body radiation therapy (SBRT) is an effective treatment for pulmonary oligometastases. Understanding the temporal evolution of computed tomography (CT) imaging features post-SBRT is crucial for optimizing patient management and improving prognostic outcomes. This study aimed to characterize the CT imaging evolution of pulmonary oligometastatic nodules following SBRT and evaluate the prognostic value of early tumor response for local control. METHODS: This multicenter retrospective study analyzed 246 pulmonary oligometastatic nodules in 191 patients treated with SBRT. We evaluated clinical characteristics, biologically effective dose (BED10), and CT imaging features, categorized by recurrence within 2 years. Tumor response at 1-month follow-up was classified as favorable [partial response (PR) or complete response (CR)] or bad [stable disease (SD) or progressive disease (PD)]. Statistical analyses included t-tests, Chi-squared tests, and Kaplan-Meier analysis. RESULTS: Significant predictors of non-recurrence included tumor diameter ≤20 mm (P<0.001), BED10 ≥100 Gy (P=0.022), and favorable early tumor response (P=0.001). The 2-year local control rate was 87.8% overall, 95.0% for nodules with a favorable early response, and 81.1% for those with a bad response. CT imaging showed that non-recurrent nodules typically exhibit early significant shrinkage, transient loose consolidation with ground-glass opacity (GGO), and eventual stable fibrosis, whereas recurrent nodules progress to mass-like consolidation. CONCLUSIONS: Favorable early response on 1-month follow-up CT, tumor diameter ≤20 mm, and BED10 ≥100 Gy are strong predictors of local control. Integrating early CT-based assessment into routine follow-up may improve recurrence detection and guide timely intervention.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。