TARGETED PROSTATE BIOPSY USING MATHEMATICAL OPTIMIZATION

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 前列腺癌是美国男性死亡的第二大原因。然而，目前没有成像模式可以在大多数情况下可靠地检测癌症。因此，当测量到升高的前列腺特异性抗原（PSA）水平时，前列腺的穿刺活检已被广泛用作前列腺癌诊断和分期的金标准。在广泛使用六分仪活检后，不同的研究组采用了几种增强的随机系统活检方法，主要是通过使用额外的活检针，以减少初始活检时未检测到的大量病例。需要更彻底地了解所有这些随机系统采样方法的性能，导致了几个计算机模拟研究，利用整个安装的组织学染色切片从子宫切除标本，以估计不同的活检方法的性能。 然而，迄今为止，还没有数学上严格的尝试来精确地确定针应该放置在哪里，以最大限度地提高癌症检测的概率。该项目的总体目标是开发和临床测试一种基于计算机的方法，用于在活检过程中对前列腺进行最佳采样，从而根据将先进的图像分析方法应用于根治性前列腺切除术标本的整体切片所获得的统计数据，最大限度地提高癌症检测的概率。因此，我们建议开发和临床测试的靶向前列腺活检方法。通过这一点，我们意味着活检部位的确切空间位置将使用数学优化方法来确定，而不是根据前列腺的粗略细分来定义近似活检位置，这是当前的实践。我们将通过以下方式实现我们的目标：1）开发和使用先进的图像分析方法，用于对大量患者的图像数据进行变形配准和统计分析，并将基于人群的图像数据映射到患者的图像上，2）在术中磁共振图像（MRI）引导下测试我们的最佳活检方法，该方法能够将针准确定位到所需位置，以及3）使用最丰富的整体切片数据库之一，这将使我们能够确定癌症分布的3D统计模型。我们的初步结果表明，癌症检测率可以显着提高使用图像分析和优化技术相结合，我们建议建立。 合作对NCIGT的好处 改进前列腺活检是临床研究的一个重要领域，对于我们的MR引导前列腺治疗计划的成功至关重要。这种合作促进了我们医院目前使用的改进的可视化和导航软件的开发。此外，我们还受益于开发有效注册软件的合作努力。我们共享并定期使用宾夕法尼亚大学的注册码，反之亦然。 对项目的好处 我们在这个项目中的作用是在临床环境中验证UPenn统计图谱。通过分析腺体的atlas目标和标准六分仪采样，我们希望能够证明基于atlas的方法提高了产量。我们能够使用术中成像来确定针相对于预期目标的精确位置，这使得这成为可能。此外，我们正在与宾夕法尼亚大学合作，以确定我们的结果是否可以用于提高统计地图集的质量。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Prostate cancer is the second leading cause of death for American men. However, there is currently no imaging modality that can reliably detect cancer in the majority of cases. Therefore, needle biopsy of the prostate has been widely used as a gold standard for the diagnosis and staging of prostate cancer, when elevated prostate specific antigen (PSA) levels are measured. Following the widespread use of sextant biopsy, several enhanced random systematic biopsy methods described later have been adopted by different groups in an effort to reduce the significant number of cases remaining undetected at initial biopsy, mainly by using additional needles. The need to more thoroughly understand the performance of all these random systematic sampling methods has led to several computer simulation studies that utilize whole mounted histologically stained sections from prostatectomy specimens in order to estimate the performance of different biopsy approaches. However, to date there has been no mathematically rigorous attempt to precisely determine where the needles should be placed in order to maximize probability of cancer detection. The overall goal of this project is to develop and clinically test a computer-based methodology for optimal sampling of the prostate during biopsy, so that the probability of cancer detection is maximal, based on statistics obtained by applying advanced image analysis methodology to whole-mounted sections of radical prostatectomy specimens. We thus propose to develop and clinically test a targeted prostate biopsy method. By this we mean that the exact spatial locations of biopsy sites will be determined using mathematical optimization methods, rather than approximate biopsy locations being defined in terms of a rough subdivision of the prostate, which is the current practice. We will achieve our goal by 1) developing and using advanced image analysis methodologies for deformable registration and statistical analysis of image data from a large number of patients, and for mapping population-based image data onto a patient's images, 2) testing our optimal biopsy approach under intraoperative magnetic resonance image (MRI) guidance, which offers the capability to accurately position a needle to a desired location, and 3) using one of the richest databases of whole-mounted sections that will allow us to determine a 3D statistical model of cancer distribution. Our preliminary results show that cancer detection rates can improve dramatically using the combination of image analysis and optimization techniques we propose to establish. Benefits of Collaboration to NCIGT Improving prostate biopsy is an important area of clinical research, and important for the success of our MR-guided prostate therapy program. This collaboration has spurred development of improved visualization and navigation software now in use in our hospital. In addition, we benefit from the collaborative effort in the development of effective registration software. We have shared and regularly use registration code from UPenn, and vice-versa. Benefits to the Project Our role in this project is to validate the UPenn statistical atlas in a clinical setting. By analyzing atlas-targeted and standard sextant sampling of the gland, we hope to be able to demonstrate that the atlas-based method improves yields. Our ability to determine the precise location of the needle with respect to the intended target using intra-operative imaging makes this possible. In addition, we are working with UPenn to determine if our results can be used to increase the quality of the statistical atlas.