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在使用表皮生长因子受体酪氨酸激酶抑制剂(EGFR-TKIs)的新辅助治疗中能够实现疗效预测的定量CT变量:它们与新辅助同步放化疗中的变量有差异吗?

Quantitative CT variables enabling response prediction in neoadjuvant therapy with EGFR-TKIs: are they different from those in neoadjuvant concurrent chemoradiotherapy?

作者信息

Chong Yousun, Kim Jae-Hun, Lee Ho Yun, Ahn Yong Chan, Lee Kyung Soo, Ahn Myung-Ju, Kim Jhingook, Shim Young Mog, Han Joungho, Choi Yoon-La

机构信息

Department of Radiology and Center for Imaging Science, Sungkyunkwan University School of Medicine, Seoul, Korea.

Department of Radiation Oncology, Sungkyunkwan University School of Medicine, Seoul, Korea.

出版信息

PLoS One. 2014 Feb 26;9(2):e88598. doi: 10.1371/journal.pone.0088598. eCollection 2014.

DOI:10.1371/journal.pone.0088598
PMID:24586348
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3935840/
Abstract

BACKGROUND AND PURPOSE

To correlate changes of various CT parameters after the neoadjuvant treatment in patients with lung adenocarcinoma with pathologic responses, focused on their relationship with different therapeutic options, particularly of EGFR-TKI and concurrent chemoradiation therapy (CCRT) settings.

MATERIALS AND METHODS

We reviewed pre-operative CT images of primary tumors and surgical specimens obtained after neoadjuvant therapy (TKI, n = 23; CCRT, n = 28) from 51 patients with lung adenocarcinoma. Serial changes in tumor volume, density, mass, skewness/kurtosis, and size-zone variability/intensity variability) were assessed from CT datasets. The changes in CT parameters were correlated with histopathologic responses, and the relationship between CT variables and histopathologic responses was compared between TKI and CCRT groups.

RESULTS

Tumor volume, mass, kurtosis, and skewness were significant predictors of pathologic response in CCRT group in univariate analysis. Using multivariate analysis, kurtosis was found to be independent predictor. In TKI group, intensity variability and size-zone variability were significantly decreased in pathologic responder group. Intensity variability was found to be an independent predictor for pathologic response on multivariate analysis.

CONCLUSIONS

Quantitative CT variables including histogram or texture analysis have potential as a predictive tool for response evaluation, and it may better reflect treatment response than standard response criteria based on size changes.

摘要

背景与目的

将肺腺癌患者新辅助治疗后各种CT参数的变化与病理反应相关联,重点关注它们与不同治疗方案的关系,特别是表皮生长因子受体酪氨酸激酶抑制剂(EGFR-TKI)和同步放化疗(CCRT)方案。

材料与方法

我们回顾了51例肺腺癌患者新辅助治疗(TKI,n = 23;CCRT,n = 28)后获得的原发性肿瘤术前CT图像和手术标本。从CT数据集中评估肿瘤体积、密度、质量、偏度/峰度以及大小区域变异性/强度变异性的系列变化。将CT参数的变化与组织病理学反应相关联,并比较TKI组和CCRT组中CT变量与组织病理学反应之间的关系。

结果

在单因素分析中,肿瘤体积、质量、峰度和偏度是CCRT组病理反应的显著预测指标。多因素分析显示,峰度是独立预测指标。在TKI组中,病理反应组的强度变异性和大小区域变异性显著降低。多因素分析显示,强度变异性是病理反应的独立预测指标。

结论

包括直方图或纹理分析在内的定量CT变量有潜力作为反应评估的预测工具,并且它可能比基于大小变化的标准反应标准更好地反映治疗反应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4f0/3935840/815e678ae2b3/pone.0088598.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4f0/3935840/2a2637daec29/pone.0088598.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4f0/3935840/f86838e371a0/pone.0088598.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4f0/3935840/815e678ae2b3/pone.0088598.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4f0/3935840/2a2637daec29/pone.0088598.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4f0/3935840/f86838e371a0/pone.0088598.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4f0/3935840/815e678ae2b3/pone.0088598.g003.jpg

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