TARGETED PROSTATE BIOPSY USING MATHEMATICAL OPTIMIZATION

使用数学优化进行靶向前列腺活检

• 批准号: 7960870
• 负责人: Christos Davatzikos
• 金额: $ 5.48万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7960870
• 关键词: Adopted; American; Area; Atlases; Biopsy; Cancer Detection; Cause of Death; Classification; Clinical; Clinical Research; Code; Collaborations; Computer Retrieval of Information on Scientific Projects Database; Computer Simulation; Computer software; Computers; Data; Databases; Development; Funding; Gland; Goals; Gold; Grant; Histologic; Hospitals; Image; Image Analysis; Imagery; Institution; Location; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Maps; Measures; Methodology; Methods; Needle biopsy procedure; Needles; Patients; Performance; Positioning Attribute; Probability; Prostate; Prostate-Specific Antigen; Prostatectomy; Radical Prostatectomy; Research; Research Personnel; Resources; Role; Sampling; Site; Source; Specimen; Staging; Staining method; Stains; Statistical Models; Techniques; Testing; United States National Institutes of Health; Work; base; diagnosis standard; image guided therapy; imaging modality; improved; men; population based; programs; statistics; success

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. Project Summary Grant Number: R01-CA104976 Project Start: 01-APR-2004 Project End: 31-MAR-2009 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 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.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 项目概要 授权号：R01-CA104976 项目启动：2004 年 4 月 1 日 项目结束：2009 年 3 月 31 日 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方式可以可靠地检测大多数情况下的癌症。因此，当测量到升高的前列腺特异性抗原（PSA）水平时，前列腺穿刺活检已被广泛用作前列腺癌诊断和分期的金标准。随着六分仪活检的广泛使用，不同群体采用了稍后描述的几种增强随机系统活检方法，主要通过使用额外的针头来努力减少初次活检时未发现的病例数量。为了更彻底地了解所有这些随机系统采样方法的性能，需要进行一些计算机模拟研究，这些研究利用前列腺切除标本的完整组织学染色切片来估计不同活检方法的性能。 然而，迄今为止，还没有在数学上进行严格的尝试来精确确定针应该放置在哪里，以便最大限度地提高癌症检测的可能性。该项目的总体目标是开发并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，从而根据对根治性前列腺切除术标本的整体切片应用先进的图像分析方法获得的统计数据，使癌症检测的概率最大化。因此，我们建议开发并临床测试一种靶向前列腺活检方法。我们的意思是，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的活检位置，这是目前的做法。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，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相结合，癌症检出率可以显着提高。 对 NCIGT 的好处 改善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也很重要。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协作努力。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宾夕法尼亚大学统计图谱。通过分析腺体的图集目标和标准六分仪采样，我们希望能够证明基于图集的方法可以提高产量。我们能够使用术中成像确定针相对于预期目标的精确位置，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。