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

摘要 迫切需要用于筛查和诊断前列腺癌（PCa）的新替代方案。电流 用于检测和诊断前列腺癌（PCa）的方法，包括血清PSA水平、DRE（指状直肠癌）、 检查）和TRUS引导（经直肠超声）随机前列腺活检存在严重缺陷，因为它们 不可靠，导致手术往往对病人没有帮助，而且经常伤害病人，费用高昂。 MRI具有改善PCa的检测和管理的潜力，因为它具有良好的软组织对比度， 功能信息。然而，到目前为止，还没有MRI方法足以用于常规筛查或 引导活检。为了在临床上有用-MRI必须识别临床上显著的癌症（Gleason 7或 更高），并将其与正常前列腺，良性变化和Gleason 6“癌症”区分开来。 在这次重新提交中，我们建议扩展我们以前在混合多维MRI（HM-MRI）上的工作， 基于T2加权和弥散加权成像的组合。这种方法与 T2和“表观扩散系数”（ADC）的常规MRI测量。常规方法治疗 T2和ADC作为独立参数。相比之下，HM-MRI测量T2的变化作为“B”的函数。 值，以及ADC的变化作为“TE”的函数。HM-MRI利用T2和ADC的相互依赖性， 前列腺组织成分的不同MR特性，以提高PCa诊断的诊断准确性。 我们将分析HM-MRI数据，以提取管腔、上皮和基质室的体积分数， 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时，已知隔室提供高诊断准确性。 为响应上次审查，对本提案作了重大修改。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、间质和前列腺组织中的前列腺癌来识别临床显著的前列腺癌。 管腔体积分数，以提供类似于定量组织学的信息。 2.此外，HM-MRI还提供了每个隔室的T2和ADC，以及体积和空间分布 这些隔间。这些信息可能会提高诊断的准确性，并且不能轻易地从 组织学 因此，HM-MRI结合房室分析可用于临床，以提供高诊断率。 准确性和PCa侵袭性的非侵入性评估。

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.