Detection of prostate Cancer Specific Signals with Hybrid Multi-Dimensional MRI

摘要 对于前列腺癌的筛查和诊断，迫切需要新的替代方案。当前 前列腺癌(PCa)的检测和诊断方法，包括血清PSA水平、直肠指端(DRE) 检查)和经直肠超声引导(经直肠超声)随机前列腺活检是有严重缺陷的，因为它们 这不仅不可靠，而且会导致程序往往无济于事，而且经常伤害患者，造成高昂的经济成本。 MRI具有良好的软组织对比度，可用于改善前列腺癌的诊断和治疗。 功能信息。然而，到目前为止，还没有一种MRI方法足以进行常规筛查或 指导活组织检查。要在临床上有用-核磁共振必须识别临床上有意义的癌症(Gleason 7或 更高)，并与正常前列腺、良性病变和格里森6癌症区分开来。 在这次重新提交中，我们建议扩展我们之前在混合多维MRI(HM-MRI)方面的工作， 基于T2加权和扩散加权成像的组合。这种方法与 常规MRI测量T2和表观弥散系数(ADC)。常规方法治疗 T2和ADC作为独立参数。相反，HM-MRI测量T2作为‘b’的函数的变化 值，以及作为‘TE’的函数的ADC的变化。HM-MRI利用T2和ADC的相互依赖关系， 明确的前列腺组织成分的磁共振特性，以提高诊断准确性的前列腺癌。 我们将分析HM-MRI数据以提取腔内、上皮和间质间隔的体积分数， 以及每个图像体素中每个隔室的ADC和T2。这些组织的体积分数 当使用定量组织学测量间隔时，已知可以提供高诊断准确率。 根据上次审查情况，对本提案进行了重大修订。我们将检验以下假设： 1.HM-MRI数据可以通过非侵入性测量上皮、间质和 腔体积分数，以提供类似于定量组织学的信息。 2.此外，HM-MRI还提供了每个间隔的T2和ADC值，以及体积和空间分布 这些车厢的。此信息可能会提高诊断的准确性，并且不容易从 组织学。 因此，HM-MRI结合间隔室分析可以在临床上提供高度的诊断。 准确、非侵入性地评估PCA侵袭性。

ABSTRACT There is a critical need for new alternatives for screening and diagnosis of prostate cancer (PCa). Current methods for detecting and diagnosing prostate cancer (PCa), including serum PSA level, DRE (digital rectal exam), and TRUS-guided (transrectal ultrasound) random prostate biopsy are seriously flawed since they are unreliable and lead to procedures that often do not help and frequently harm patients, at high financial costs. MRI has potential to improve detection and management of PCa, due to its excellent soft tissue contrast and functional information. Nevertheless there is, as of yet, no MRI method that is adequate for routine screening or for guiding biopsies. To be clinically useful –MRI must identify clinically significant cancers (Gleason 7 or higher) and distinguish them from normal prostate, benign changes, and Gleason 6 ‘cancers’. In this resubmission, we propose to extend our previous work on hybrid multi-dimensional MRI (HM-MRI), based on the combination of T2-weighted and diffusion-weighted imaging. This approach is very different from conventional MRI measurements of T2 and ‘apparent diffusion coefficient’ (ADC). Conventional methods treat T2 and ADC as independent parameters. In contrast, HM-MRI measures the change in T2 as a function of ‘b’ value, and the change in ADC as a function of ‘TE’. HM-MRI exploits the interdependence of T2 and ADC and distinct MR properties of prostate tissue components to increase diagnostic accuracy of PCa diagnosis. We will analyze HM-MRI data to extract volume fractions of the luminal, epithelial, and stromal compartments, and the ADC and T2 of each compartment in each image voxel. Volume fractions of these tissue compartments, when measured using quantitative histology, are known to provide high diagnostic accuracy. This proposal is significantly revised to respond to the previous review. We will test the hypotheses that: 1. HM-MRI data can identify clinically significant PCa, by non-invasively measuring epithelial, stromal, and luminal volume fractions, to provide information similar to quantitative histology. 2. In addition, HM-MRI provides the T2 and ADC of each compartment, and the volume and spatial distribution of these compartments. This information may increase diagnostic accuracy, and cannot be easily obtained from histology. As a result, HM-MRI combined with compartmental analysis can be used clinically to provide high diagnostic accuracy, and non-invasive assessment of PCa aggressiveness.