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.
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