Strain elastography as an early predictor of long-term prognosis in patients with locally advanced cervical cancers treated with concurrent chemoradiotherapy

European radiology(2019)

引用 9|浏览42
暂无评分
摘要
Objective To explore the value of strain elastography as an early predictor of long-term prognosis in patients with locally advanced cervical cancers treated with concurrent chemoradiotherapy (CCRT). Methods Strain elastography examinations were performed on 45 patients with locally advanced cervical cancers at 3 time points: prior to CCRT, and at 1 and 2 weeks after the start of CCRT. The maximum tumor diameter ( D max ), strain ratio (SR), and their percentage changes (Δ D max and ΔSR) were calculated to predict long-term prognosis. Based on the results of physical examinations, Papanicolaou test, and pelvic magnetic resonance imaging, we classified patients into two groups: responders (complete remission) and non-responders (sustained disease, recurrence, or death). Results After a median follow-up of 30 months (range, 12–36 months), 36 of 45 (80%) patients were disease free. The D max as well as Δ D max at 2 weeks during CCRT was able to predict the responder outcomes, with an area-under-the-curve (AUC) of 0.733 and 0.731, respectively. Furthermore, significant differences in SR and ΔSR at 1 and 2 weeks during therapy were shown between the responder and non-responder groups (all p < 0.05), and ΔSR at 2 weeks during CCRT presented with the highest AUC (0.91), yielding 88.9% sensitivity and 88.9% specificity with a selected cutoff value. Conclusions Strain elastography may be useful as an early predictor of long-term outcomes after CCRT for patients with cervical cancer. Key Points • The D max as well as ΔD max at 2 weeks during CCRT can predict the responder outcomes. • The elastography parameters (SR and ΔSR) exhibited predictive values of favorable response after therapy initiation. • ΔSR at 2 weeks during CCRT held the best predictive value for the responder outcomes.
更多
查看译文
关键词
Elastography,Treatment outcome,Cervical cancer,Concurrent chemoradiotherapy
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