CT texture analysis in colorectal liver metastases: A better way than size and volume measurements to assess response to chemotherapy?
CT texture analysis in colorectal liver metastases: A better way than size and volume measurements to assess response to chemotherapy?
复制标题
DOI：
10.1177/2050640615601603
复制
发表时间：
2016-04-01
影响因子：
6
通讯作者：
Beets-Tan, Regina G. H.
中科院分区：
文献类型：
--
作者：
Rao, Sheng-Xiang;Lambregts, Doenja M. J.;Beets-Tan, Regina G. H.
众所周知，实体瘤背景反应评估标准 (RECIST) 在评估结直肠肝转移瘤 (CRLM) 对化疗的反应方面存在局限性。 目的 本文的目的是将 CT 纹理分析与基于 RECIST 的尺寸测量和肿瘤体积测量进行比较，以评估 CRLM 对化疗的反应。 方法 21 名 CRLM 患者在化疗前和化疗后接受了 CT 扫描。使用不同的过滤值（0.0=无/0.5=细/1.5=中/2.5=粗过滤）评估最大转移病灶的纹理参数平均强度（M）、熵（E）和均匀性（U）。确定所有转移病灶的总体积（cm（3））和一到两个病灶的最大尺寸（根据RECIST 1.1）。通过单变量逻辑回归分析确定了区分良好反应者（n=9；组织学 TRG 1-2）与不良反应者（n=12；TRG 3-5）的潜在预测参数，并随后在多变量逻辑回归分析中进行测试。记录诊断比值比。结果最好的预测纹理参数是均匀性和熵（无过滤）。多变量分析中均匀性和熵的优势比分别为 0.95 和 1.34。处理前和处理后的纹理参数以及各种尺寸和体积测量并不是显着的预测因素。单变量逻辑回归中大小和体积的优势比分别为 1.08 和 1.05。 结论 治疗后 CT 纹理的相对差异有望评估 CRLM 患者对化疗的病理反应，并且可能比病灶大小或体积的变化更好地预测反应。
Background Response Evaluation Criteria In Solid Tumors (RECIST) are known to have limitations in assessing the response of colorectal liver metastases (CRLMs) to chemotherapy.Objective The objective of this article is to compare CT texture analysis to RECIST-based size measurements and tumor volumetry for response assessment of CRLMs to chemotherapy.Methods Twenty-one patients with CRLMs underwent CT pre- and post-chemotherapy. Texture parameters mean intensity (M), entropy (E) and uniformity (U) were assessed for the largest metastatic lesion using different filter values (0.0=no/0.5=fine/1.5=medium/2.5=coarse filtration). Total volume (cm(3)) of all metastatic lesions and the largest size of one to two lesions (according to RECIST 1.1) were determined. Potential predictive parameters to differentiate good responders (n=9; histological TRG 1-2) from poor responders (n=12; TRG 3-5) were identified by univariable logistic regression analysis and subsequently tested in multivariable logistic regression analysis. Diagnostic odds ratios were recorded.Results The best predictive texture parameters were uniformity and entropy (without filtration). Odds ratios for uniformity and entropy in the multivariable analyses were 0.95 and 1.34, respectively. Pre- and post-treatment texture parameters, as well as the various size and volume measures, were not significant predictors. Odds ratios for size and volume in the univariable logistic regression were 1.08 and 1.05, respectively.Conclusions Relative differences in CT texture occurring after treatment hold promise to assess the pathologic response to chemotherapy in patients with CRLMs and may be better predictors of response than changes in lesion size or volume.