Machireddy Archana, Thibault Guillaume, Tudorica Alina, Afzal Aneela, Mishal May, Kemmer Kathleen, Naik Arpana, Troxell Megan, Goranson Eric, Oh Karen, Roy Nicole, Jafarian Neda, Holtorf Megan, Huang Wei, Song Xubo
Oregon Health and Science University, Portland, OR.
Tomography. 2019 Mar;5(1):90-98. doi: 10.18383/j.tom.2018.00046.
We aimed to determine whether multiresolution fractal analysis of voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps can provide early prediction of breast cancer response to neoadjuvant chemotherapy (NACT). In total, 55 patients underwent 4 DCE-MRI examinations before, during, and after NACT. The shutter-speed model was used to analyze the DCE-MRI data and generate parametric maps within the tumor region of interest. The proposed multiresolution fractal method and the more conventional methods of single-resolution fractal, gray-level co-occurrence matrix, and run-length matrix were used to extract features from the parametric maps. Only the data obtained before and after the first NACT cycle were used to evaluate early prediction of response. With a training (N = 40) and testing (N = 15) data set, support vector machine was used to assess the predictive abilities of the features in classification of pathologic complete response versus non-pathologic complete response. Generally the multiresolution fractal features from individual maps and the concatenated features from all parametric maps showed better predictive performances than conventional features, with receiver operating curve area under the curve (AUC) values of 0.91 (all parameters) and 0.80 (K), in the training and testing sets, respectively. The differences in AUC were statistically significant ( < .05) for several parametric maps. Thus, multiresolution analysis that decomposes the texture at various spatial-frequency scales may more accurately capture changes in tumor vascular heterogeneity as measured by DCE-MRI, and therefore provide better early prediction of NACT response.
我们旨在确定基于体素的动态对比增强磁共振成像（DCE-MRI）参数图的多分辨率分形分析是否能够对乳腺癌新辅助化疗（NACT）的反应提供早期预测。共有55例患者在NACT治疗前、治疗期间和治疗后接受了4次DCE-MRI检查。采用快门速度模型分析DCE-MRI数据，并在感兴趣的肿瘤区域内生成参数图。使用所提出的多分辨率分形方法以及更传统的单分辨率分形、灰度共生矩阵和游程矩阵方法从参数图中提取特征。仅使用第一个NACT周期前后获得的数据来评估反应的早期预测。通过训练（N = 40）和测试（N = 15）数据集，使用支持向量机评估这些特征在病理完全缓解与非病理完全缓解分类中的预测能力。一般来说，来自单个图的多分辨率分形特征以及来自所有参数图的串联特征比传统特征表现出更好的预测性能，在训练集和测试集中，曲线下面积（AUC）值分别为0.91（所有参数）和0.80（K）。对于几个参数图，AUC的差异具有统计学意义（<0.05）。因此，在不同空间频率尺度上分解纹理的多分辨率分析可能更准确地捕捉通过DCE-MRI测量的肿瘤血管异质性变化，从而为NACT反应提供更好的早期预测。
