TARGETED PROSTATE BIOPSY USING MATHEMATICAL OPTIMIZATION

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 前列腺癌是美国男性的第二大死因。然而，目前还没有一种成像手段可以在大多数情况下可靠地检测出癌症。因此，当前列腺癌患者的前列腺特异性抗原(PSA)水平升高时，前列腺活检已被广泛用作前列腺癌诊断和分期的金标准。随着六分仪活检术的广泛使用，不同的组织已经采用了几种稍后描述的增强型随机系统活检法，以努力减少首次活检术中未发现的大量病例，主要是通过使用额外的针头。为了更彻底地了解所有这些随机系统抽样方法的性能，已经进行了几项计算机模拟研究，这些研究利用前列腺切除标本的完整组织学染色切片来评估不同活检方法的性能。 然而，到目前为止，还没有严格的数学尝试来精确地确定针头应该放在哪里，以便最大限度地提高癌症检测的可能性。这个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，从而使癌症检测的可能性最大，基于将先进的图像分析方法应用于前列腺癌根治术标本的整体切片所获得的统计数据。因此，我们建议开发和临床测试一种有针对性的前列腺活检方法。我们的意思是，活检部位的准确空间位置将使用数学优化方法确定，而不是像目前的做法那样，根据前列腺的粗略细分来定义大致的活检位置。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，对大量患者的图像数据进行可变形登记和统计分析，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)指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；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，使用我们建议建立的图像分析和优化技术的组合，癌症检测率可以显著提高。 协作给NCIGT带来的好处 改进前列腺活检是临床研究的一个重要领域，对我们的MR引导的前列腺治疗计划的成功也很重要。这种合作促进了改进的可视化和导航软件的开发，目前正在我们医院使用。此外，我们还受益于在开发有效注册软件方面的合作努力。我们共享并经常使用来自宾夕法尼亚大学的注册码，反之亦然。 给项目带来的好处 我们在这个项目中的作用是在临床环境中验证宾夕法尼亚大学的统计图谱。通过分析atlas靶标和标准六分仪对腺体的采样，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定目标的精确位置，从而使这成为可能。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.