DCE-MRI 时间参数对肿瘤血管化异质性定量的影响。
Influence of temporal parameters of DCE-MRI on the quantification of heterogeneity in tumor vascularization.
机构信息
Department of Radiology, Institut Bergonié, Comprehensive Cancer Center, Bordeaux, France.
University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project Team Monc, Talence, France.
出版信息
J Magn Reson Imaging. 2019 Dec;50(6):1773-1788. doi: 10.1002/jmri.26753. Epub 2019 Apr 13.
BACKGROUND
Evaluating heterogeneity in tumor vascularization through texture analysis could improve predictions of patients' outcome and response evaluation.
PURPOSE
To investigate the influence of temporal parameters on texture features extracted from dynamic contrast-enhanced (DCE)-MRI parametric maps.
STUDY TYPE
Prospective cross-sectional study.
SUBJECTS
Twenty-five adults with soft-tissue sarcoma (STS), median age: 68 years.
FIELD STRENGTH/SEQUENCE: DCE-MRI acquisition using a CAIPIRINHA-Dixon-TWIST-VIBE sequence at 1.5T (temporal resolutions: 2 sec, duration: 5 min).
ASSESSMENT
The area under time-intensity curve (AUC) and K maps were generated for several temporal resolution (dt = 2 sec, 4 sec, 6 sec, 8 sec, 10 sec, 12 sec, 20 sec) and scan durations (T = 3 min, 4 min, 5 min for a 6-sec sampling) by downsampling and truncating the initial DCE-MRI sequence. Tumor volume was manually segmented and propagated on all parametric maps. Thirty-two first- and second order-texture features were extracted per map to quantify the intratumoral heterogeneity.
STATISTICAL TESTS
The influence of temporal parameters on texture features was studied with repeated-measures analysis of variance (or nonparametric equivalent). The dispersion of each texture feature depending on temporal parameters was estimated with coefficients of variation (CVs). The performances of multivariate models to predict the response to chemotherapy (ie, binary logistic regression based on the baseline texture features) were compared.
RESULTS
The temporal resolution had a significant influence on 12/32 (37.5%) and 14/32 (43.8%) texture features evaluated on AUC and K maps, respectively (range of P < 0.0001-0.0395). Scan duration had a significant influence on 23/32 (71.9%) texture features from K map (range of P < 0.0001-0.0321). Dispersion was high (mean CV >0.5) with sampling for 2/32 (6.3%) and 10/32 (31.3%) features from AUC and K maps, respectively; and with truncating for 6/32 (18.8%) features from K map. The area under the receiver operating characteristics curve of predictive models ranged from 0.77 (95% confidence interval [CI] = [0.54-1.00], with dt = 6 sec T = 4 min) to 0.90 (95% CI = [0.74-1.00], with dt = 6 sec T = 5 min).
DATA CONCLUSION
The values of texture features extracted from DCE-MRI parametric maps can be influenced by temporal parameters, which can lead to variations in performance of predictive models.
LEVEL OF EVIDENCE
2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1773-1788.
背景
通过纹理分析评估肿瘤血管生成的异质性可以提高对患者预后和反应评估的预测。
目的
研究时间参数对从动态对比增强（DCE）-MRI 参数图中提取的纹理特征的影响。
研究类型
前瞻性横断面研究。
受试者
25 名患有软组织肉瘤（STS）的成年人，中位年龄：68 岁。
磁场强度/序列：在 1.5T 下使用 CAIPIRINHA-Dixon-TWIST-VIBE 序列进行 DCE-MRI 采集（时间分辨率：2 秒，持续时间：5 分钟）。
评估
通过对初始 DCE-MRI 序列进行下采样和截断，为多个时间分辨率（dt = 2 秒、4 秒、6 秒、8 秒、10 秒、12 秒、20 秒）和扫描时间（T = 3 分钟、4 分钟、5 分钟，用于 6 秒采样）生成时间-强度曲线（AUC）和 K 图。手动分割肿瘤体积并在所有参数图上传播。对每个图提取 32 个一阶和二阶纹理特征，以量化肿瘤内异质性。
统计检验
采用重复测量方差分析（或等效的非参数检验）研究时间参数对纹理特征的影响。通过变异系数（CV）估计每个纹理特征随时间参数的离散度。比较基于基线纹理特征的二元逻辑回归（即，基于基线纹理特征的二进制逻辑回归）来预测化疗反应的多变量模型的性能。
结果
时间分辨率对 AUC 和 K 图上分别评估的 12/32（37.5%）和 14/32（43.8%）纹理特征有显著影响（范围 P <0.0001-0.0395）。扫描持续时间对 K 图上的 23/32（71.9%）纹理特征有显著影响（范围 P <0.0001-0.0321）。对于 AUC 和 K 图上的 2/32（6.3%）和 10/32（31.3%）特征，采样的离散度较高（平均 CV>0.5）；对于 K 图上的 6/32（18.8%）特征，截断的离散度较高。预测模型的受试者工作特征曲线下面积范围为 0.77（95%置信区间[CI] = [0.54-1.00]，dt = 6 秒 T = 4 分钟）至 0.90（95% CI = [0.74-1.00]，dt = 6 秒 T = 5 分钟）。
数据结论
从 DCE-MRI 参数图中提取的纹理特征的值可能会受到时间参数的影响，这可能会导致预测模型性能的变化。
证据水平
2 技术功效：第 2 阶段 J. Magn. Reson. Imaging 2019;50:1773-1788.
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