Symptom relief, prognostic factors, and outcome in patients receiving urgent radiation therapy for superior vena cava syndrome

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al](2022)

引用 2|浏览8
暂无评分
摘要
Purpose Superior vena cava syndrome (SVCS) often results from external vessel compression due to tumor growth. Urgent symptom-guided radiotherapy (RT) remains a major treatment approach in histologically proven, rapidly progressive disease. Despite several publications, recent data concerning symptom relief and oncological outcome as well as potential confounders in treatment response are still scarce. Methods We performed a retrospective single-center analysis of patients receiving urgent RT between 2000 and 2021 at the University Medical Center Göttingen. Symptom relief was evaluated by CTCAE score during the RT course. Effects of variables on symptom relief were assessed by logistic regression. The impact of parameters on overall survival (OS) was evaluated using Kaplan–Meier plot along with the log-rank test and by Cox regression analyses. Statistically significant ( p -value < 0.05) confounders were tested in multivariable analyses. Results A total of 79 patients were included. Symptom relief was achieved in 68.4%. Mean OS was 59 days, 7.6% ( n = 6) of patients showed long-term survival (> 2 years). Applied RT dose > 39 Gy, clinical target volume (CTV) size < 387 ml, concomitant chemotherapy, and completion of the prescribed RT course were found to be statistically significant for OS; applied RT dose and completion of the prescribed RT course were found to be statistically significant for symptom relief. Conclusion Symptom relief by urgent RT for SVCS was achieved in the majority of patients. RT dose and completion of the RT course were documented as predictors for OS and symptom relief, CTV < 387 ml and concomitant chemotherapy were predictive for OS.
更多
查看译文
关键词
Superior Vena Cava Syndrome,Radiotherapy,Symptom relief,Retrospective
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要