Improving the Specificity of Dynamic MRI in Breast Cancer Diagnosis

描述（由申请人提供）：本项目旨在显著提高动态MRI检测和诊断乳腺癌的特异性。虽然目前的动态乳腺MRI在检测乳腺癌增强方面具有较高的灵敏度，但其在癌症诊断中的特异性有限。这部分是由于用于动态乳腺MRI中的增强分割的当前配准和分割方法的局限性。值得注意的是，图像配准和分割的准确性不仅影响对比度增强的分割，而且最终影响对比度增强的良性或恶性分类，如文献中所证明的。更好的图像配准和分割允许更准确地分割增强病灶，并在理论上减少假阴性。另一方面，当前动态乳腺MRI的低特异性也与当前仅使用动态动力学参数或形态学特征或其简单组合用于癌症诊断的增强分类方法的局限性有关。更重要的是，据我们所知，几乎所有的癌症诊断方法都是针对仅肿块增强而不是非肿块增强的分类而设计的。本项目的目标是通过开发两个新的增强分割和分类模块，分别克服这些限制。在目标1中，将开发高级增强分割模块，用于以高灵敏度和特异性分割潜在可疑增强。特别是，将开发一种新的时空配准方法，用于在图像采集过程中对患者运动进行一致的估计，并且还将开发一种基于图形切割的分割技术，用于增强区域的自适应分割。在目标2中，将开发一种新的增强分类模块，通过考虑其各自的增强形态，区分肿块和非肿块增强病例的良性增强和恶性增强。特别是，将从动态MRI序列中完全捕获肿瘤结构的演变模式并用于增强分类，而不是仅使用文献中普遍使用的简单区域动态特征。这两个模块的有效性将通过现有的数据库进行评估，该数据库包含来自700多名患者的MRI扫描。公共卫生相关性：该项目旨在显著提高动态MRI检测和诊断乳腺癌的特异性。特别是，将开发一个先进的增强分割模块，用于以高灵敏度和特异性分割潜在的可疑增强。并且，将开发一种新的增强分类模块，以通过考虑其各自的增强形态来区分肿块和非肿块增强病例的良性与恶性增强。

DESCRIPTION (provided by applicant): This project aims at significant improvement of specificity of dynamic MRI in detecting and diagnosing breast cancer. Although current dynamic breast MRI has high sensitivity in detecting enhancement of breast cancer, it has limited specificity in cancer diagnosis. This is partially due to the limitations of the current registration and segmentation methods used for enhancement segmentation in the dynamic breast MRI. It is worth noting that the accuracy of image registration and segmentation affects not only the segmentation of contrast enhancement, but also eventually the classification of contrast enhancement as benign or malignant, as having been proved in the literature. Better image registration and segmentation allow more accurate segmentation of foci of enhancement and in theory reduce false negatives. On the other hand, the low specificity of current dynamic breast MRI is also related to the limitation of current enhancement classification methods which use only the dynamic kinetic parameters or morphologic features, or their simple combinations for cancer diagnosis. More importantly, according to our knowledge, almost all cancer diagnosis methods were designed for classification of only mass enhancement, not the non-mass enhancement. The goal of this project is to overcome these limitations by developing two novel modules for enhancement segmentation and classification, respectively. In Aim 1, an advanced enhancement segmentation module will be developed for segmenting potentially suspicious enhancement with high sensitivity and specificity. In particular, a novel spatiotemporal registration method will be developed for consistent estimation of patient motion during the image acquisition, and also a graph-cut based segmentation technique will be developed for adaptive segmentation of enhancement regions. In Aim 2, a novel enhancement classification module will be developed to differentiate benign enhancement from malignant enhancement for both mass and non-mass enhancement cases by considering their respective enhancement morphologies. In particular, the evolving pattern of tumor architecture will be completely captured from dynamic MRI sequence and used for enhancement classification, instead of using only simple region-wise dynamic features as popularly used in the literature. The efficacy of these two modules will be evaluated by an existing database with MRI scans from over 700 patients. PUBLIC HEALTH RELEVANCE: This project aims at significant improvement of specificity of dynamic MRI in detecting and diagnosing breast cancer. In particular, an advanced enhancement segmentation module will be developed for segmenting potentially suspicious enhancement with high sensitivity and specificity. And, a novel enhancement classification module will be developed to differentiate benign from malignant enhancement for both mass and non- mass enhancement cases by considering their respective enhancement morphologies.