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Early DCE-MRI Changes after Longitudinal Registration May Predict Breast Cancer Response to Neoadjuvant Chemotherapy

机译:纵向注册后早期DCE-MRI改变可能预测乳腺癌对新辅助化疗的反应

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To monitor tumor response to neoadjuvant chemotherapy, investigators have begun to employ quantitative physiological parameters available from dynamic contrast enhanced MRI (DCE-MRI). However, most studies track the changes in these parameters obtained from the tumor region of interest (ROI) or histograms, thereby discarding all spatial information on tumor heterogeneity. In this study, we applied a nonrigid registration to longitudinal DCE-MRI data and performed a voxel-by-voxel analysis to examine the ability of early changes in parameters at the voxel level to separate pathologic complete responders (pCR) from non-responders (NR). Twenty-two patients were examined using DCE-MRI pre-, post one cycle, and at the conclusion of all neoadjuvant chemotherapy. The fast exchange regime model (FXR) was applied to both the original and registered DCE-MRI data to estimate tumor-related parameters. The results indicate that compared with the ROI analysis, the voxel-based analysis after longitudinal registration may improve the ability of DCE-MRI to separate complete responders from non-responders after one cycle of therapy when using the FXR model (p = 0.02).
机译:为了监测肿瘤对新辅助化疗的反应,研究人员已开始采用可从动态对比增强MRI(DCE-MRI)获得的定量生理参数。但是,大多数研究跟踪从感兴趣的肿瘤区域(ROI)或直方图获得的这些参数的变化,从而放弃了有关肿瘤异质性的所有空间信息。在这项研究中,我们对纵向DCE-MRI数据应用了非刚性配准,并进行了逐个体素分析,以检查在体素水平上参数的早期变化将病理完全反应者(pCR)与非反应者分开的能力( NR)。在一个周期之前,之后以及所有新辅助化疗结束时,使用DCE-MRI检查了22例患者。快速交换方案模型(FXR)应用于原始和注册的DCE-MRI数据,以估计肿瘤相关参数。结果表明,与ROI分析相比,纵向注册后基于体素的分析可能会提高使用FXR模型治疗一个周期后DCE-MRI将完全响应者与非响应者分开的能力(p = 0.02)。

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