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诱导化疗两周期后行定量 CT 评价对局部进展期食管鳞癌根治性放化疗/放疗前预测预后的价值

Quantitative CT evaluation after two cycles of induction chemotherapy to predict prognosis of patients with locally advanced oesophageal squamous cell carcinoma before undergoing definitive chemoradiotherapy/radiotherapy.

机构信息

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52, Fucheng Road, Hai Dian District, Beijing, 100142, China.

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Early Drug Development Center, Peking University Cancer Hospital & Institute, No. 52, Fucheng Road, Haidian District, Beijing, 100142, China.

出版信息

Eur Radiol. 2023 Jan;33(1):380-390. doi: 10.1007/s00330-022-08994-y. Epub 2022 Aug 4.

DOI:10.1007/s00330-022-08994-y
PMID:35927466
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9755097/
Abstract

OBJECTIVE

To investigate the performance of quantitative CT analysis in predicting the prognosis of patients with locally advanced oesophageal squamous cell carcinoma (ESCC) after two cycles of induction chemotherapy before definitive chemoradiotherapy/radiotherapy.

METHODS

A total of 110 patients with locally advanced ESCC were retrospectively analysed. Baseline chest CT and CT after two cycles of induction chemotherapy were analysed. A multivariate Cox proportional-hazard regression model was used to identify independent prognostic markers for survival analysis. Then, a CT scoring system was established. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan-Meier method were employed for analysing the prognostic value of the CT scoring system.

RESULTS

Body mass index, treatment strategy, change ratios of thickness (ΔTH), CT value of the primary tumour (ΔCTV) and the short diameter (ΔSD-LN), and the presence of an enlarged small lymph node (ESLN) after two cycles of chemotherapy were noted as independent factors for predicting overall survival (OS). The specificity of the presence of ESLN for death after 12 months was up to 100%. Areas under the curve value of the CT scoring system for predicting OS and progression-free survival (PFS) were higher than that of the RECIST (p < 0.05). Responders had significantly longer OS and PFS than non-responders.

CONCLUSION

Quantitative CT analysis after two cycles of induction chemotherapy could predict the outcome of locally advanced ESCC patients treated with definitive chemoradiotherapy/radiotherapy. The CT scoring system could contribute to the development of an appropriate strategy for patients with locally advanced ESCC.

KEY POINTS

• Quantitative CT evaluation after two cycles of induction chemotherapy can predict the long-term outcome of locally advanced oesophageal cancer treated with definitive chemoradiotherapy/radiotherapy. • A CT scoring system provides valuable imaging support for indicating the prognosis at the early stage of therapy. • Quantitative CT evaluation can assist clinicians in personalising treatment plans.

摘要

目的

探讨定量 CT 分析在预测根治性放化疗/放疗前两周期诱导化疗后局部晚期食管鳞癌(ESCC)患者预后中的作用。

方法

回顾性分析 110 例局部晚期 ESCC 患者,分析基线胸部 CT 和两周期诱导化疗后 CT。采用多变量 Cox 比例风险回归模型对生存分析的独立预后标志物进行识别。然后,建立 CT 评分系统。采用时间依赖性接受者操作特征(ROC)曲线分析和 Kaplan-Meier 法分析 CT 评分系统的预后价值。

结果

体重指数、治疗策略、厚度变化比(ΔTH)、原发肿瘤 CT 值变化(ΔCTV)和短径变化(ΔSD-LN)以及两周期化疗后肿大的小淋巴结(ESLN)的存在是预测总生存(OS)的独立因素。ESLN 存在对 12 个月后死亡的特异性高达 100%。CT 评分系统预测 OS 和无进展生存(PFS)的曲线下面积值高于 RECIST(p<0.05)。缓解者的 OS 和 PFS 明显长于非缓解者。

结论

两周期诱导化疗后定量 CT 分析可预测接受根治性放化疗/放疗的局部晚期 ESCC 患者的结局。CT 评分系统有助于为局部晚期 ESCC 患者制定合适的策略。

关键点

• 两周期诱导化疗后定量 CT 评估可预测局部晚期食管癌根治性放化疗/放疗后长期预后。

• CT 评分系统为早期治疗提供有价值的影像学支持,以提示预后。

• 定量 CT 评估可协助临床医生制定个体化治疗方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3e3/9755097/c4efd0219988/330_2022_8994_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3e3/9755097/765c4cb83414/330_2022_8994_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3e3/9755097/3d443421d558/330_2022_8994_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3e3/9755097/c4efd0219988/330_2022_8994_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3e3/9755097/765c4cb83414/330_2022_8994_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3e3/9755097/3d443421d558/330_2022_8994_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3e3/9755097/c4efd0219988/330_2022_8994_Fig3_HTML.jpg

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