Targeted prostate biopsy using mathematical optimization

描述(由申请人提供)： 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，不同群体采用了几种增强的随机系统活检法，以努力减少初次活检术中仍未发现的大量病例，主要是通过使用额外的针头，尽管收效甚微。为了更彻底地了解所有这些随机系统抽样方法的性能，已经有了几项计算机模拟研究，这些研究利用前列腺切除标本的整体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。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，以便根据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而获得的统计数据，最大限度地检测出癌症。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。来自281个不同标本的组织切片将被用来确定前列腺癌的空间统计。将开发一种用于弹性形状变换的方法，并通过消除个体间的形态差异来准确地覆盖来自不同样本的图像。最佳活检点将通过数学优化来确定，即通过寻找最大限度地提高癌症检测概率的针坐标，并考虑到针放置的预期误差。可变形配准算法将被开发用于将最佳活检位置叠加在患者的MR图像上。在精确的术中MR指导下，最优抽样方法将在独立的患者群体上得到验证。

DESCRIPTION (provided by applicant): 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 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, albeit with limited success. 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 histological 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 an 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. Histological sections from 281 diverse specimens will be used to determine the spatial statistics of prostate cancer. A methodology for elastic shape transformation will be developed and used to accurately overlay images from different specimens, by removing inter-individual morphological variability. Optimal biopsy sites will be determined by mathematical optimization, i.e., by finding the needle coordinates that maximize probability of cancer detection, taking into consideration expected errors in needle placement. Deformable registration algorithms will be developed for overlaying the optimal biopsy sites on a patient's MR images. The optimal sampling method will be validated on an independent patient population, under precise intra-operative MR guidance.