Computer Science Unit, Faculty of Engineering, University of Mons, Mons, Belgium.
Jules Bordet Institute, Brussels, Belgium.
Int J Comput Assist Radiol Surg. 2018 Aug;13(8):1233-1243. doi: 10.1007/s11548-018-1790-y. Epub 2018 May 22.
This study aims to provide and optimize a performing algorithm for predicting the breast cancer response rate to the first round of chemotherapy using Magnetic Resonance Imaging (MRI). This provides an early recognition of breast tumor reaction to chemotherapy by using the Parametric Response Map (PRM) method.
PRM may predict the breast cancer response to chemotherapy by analyzing voxel-by-voxel temporal intra-tumor changes during one round of chemotherapy. Indeed, the tumor recognizes intra-tumor changes concerning its vascularity, which is an important criterion in the present study. This method is mainly based on spatial image affine registration between the breast tumor MRI volumes, acquired before and after the first cycle of chemotherapy, and region growing segmentation of the tumor volume. To evaluate our method, we used a retrospective study of 40 patients provided by a collaborating institute.
PRM allows a color map to be created with the percentages of positive, negative and stable breast tumor response during the first round of chemotherapy, identifying each region with its response rate. We assessed the accuracy of the proposed method using technical and medical validation methods. The technical validation was based on landmarks-based registration and fully manual segmentation. The medical evaluation was based on the accuracy calculation of the standard reference of anatomic pathology. The p-values and the Area Under the Curve (AUC) of the Receiver Operating Characteristics were calculated to evaluate the proposed PRM method.
We performed and evaluated the proposed PRM method to study and analyze the behavior of a tumor during the first round of chemotherapy, based on the intra-tumor changes of MR breast tumor images. The AUC obtained for the PRM method is considered as relevant in the early prediction of breast tumor response.
本研究旨在提供并优化一种使用磁共振成像（MRI）预测乳腺癌对第一轮化疗反应的算法。这通过使用参数响应图（PRM）方法来实现对乳腺癌肿瘤对化疗的早期识别。
PRM 可以通过分析一轮化疗过程中肿瘤内每个体素的时间性变化来预测乳腺癌对化疗的反应。实际上，肿瘤识别其血管生成的肿瘤内变化，这是本研究中的一个重要标准。该方法主要基于化疗前和化疗后乳房肿瘤 MRI 体积之间的空间图像仿射配准，以及肿瘤体积的区域生长分割。为了评估我们的方法，我们使用了一个合作机构提供的 40 名患者的回顾性研究。
PRM 可以创建一个颜色映射图，显示第一轮化疗期间乳腺癌肿瘤的阳性、阴性和稳定反应的百分比，确定每个区域的反应率。我们使用基于地标配准和完全手动分割的技术验证和基于解剖病理学标准参考的医学评估方法来评估所提出方法的准确性。计算了 p 值和接收器操作特性曲线下的面积（AUC），以评估所提出的 PRM 方法。
我们对所提出的 PRM 方法进行了研究和分析，以研究和分析肿瘤在第一轮化疗过程中的行为，基于 MR 乳房肿瘤图像的肿瘤内变化。所提出的 PRM 方法的 AUC 被认为是预测乳腺癌肿瘤反应的早期指标。
