Optimizing prostate needle biopsy through 3D simulation

Prostate needle biopsy is used for the detection of prostate cancer. The protocol of needle biopsy that is currently routinely used in the clinical environment is the systematic sextant technique, which defines six symmetric locations on the prostate surface for needle insertion. However, this protocol has been developed based on the long-term observation and experience of urologists. Little quantitative or scientific evidence supports the use of this biopsy technique. In this research, we aim at developing a statistically optimized new prostate needle biopsy protocol to improve the quality of diagnosis of prostate cancer. This new protocol will be developed by using a three-dimensional (3-D) computer- based probability map of prostate cancer. For this purpose, we have developed a computer-based 3-D visualization and simulation system with prostate models constructed from the digitized prostate specimens, in which the process of prostate needle biopsy can be simulated automatically by the computer. In this paper, we first develop an interactive biopsy simulation mode in the system, and evaluate the performance of the automatic biopsy simulation with the sextant biopsy protocol by comparing the results by the urologist using the interactive simulation mode with respect to 53 prostate models. This is required to confirm that the automatic simulation is accurate and reliable enough for the simulation with respect to a large number of prostate models. Then we compare the performance of the existing protocols using the automatic biopsy simulation system with respect to 107 prostate models, which will statistically identify if one protocol is better than another. Since the estimation of tumor volume is extremely important in determining the significance of a tumor and in deciding appropriate treatment methods, we further investigate correlation between the tumor volume and the positive core volume with 89 prostate models. This is done in order to develop a method to estimate the tumor volume from the corresponding positive core volumes. Finally, we propose an algorithm for developing a statistically optimized prostate needle biopsy protocol. Preliminary experimental results are also presented.