Statistically optimized biopsy strategy for the diagnosis of prostate cancer

Presents a method for optimizing prostate needle biopsy, by creating a statistical atlas of the spatial distribution of prostate cancer from a large patient cohort. In order to remove inter-individual morphological variability and to determine the true variability in the spatial distribution of cancer within the prostate, an adaptive-focus deformable model (AFDM) is first used to register and normalize the prostate samples. A probabilistic method is then developed to select the prostate biopsy strategy that the greatest chance of detecting prostate cancer. For a test set of data from 20 prostate subjects, five needle locations are adequate to detect the tumor 100% of the time. Furthermore, results on the accuracy of deformable registration and the predictive power of our statistically optimized biopsy strategy are presented in this paper.

1. Introduction

Prostate cancer is the second leading cause of death for American men [1]. When prostate cancer is diagnosed early, it is usually curable. Therefore, the decision of who and when to treat for prostate cancer is very important. Transrectal-Ultrasonography-guided symmetric needle biopsy has been widely used as a gold standard for the diagnosis of prostate cancer. However, biopsy is currently performed in a rather empirical way, since cancer is mostly undetectable in the routinely used ultrasound images. Thus, biopsy protocols that designate locations of the needles within the prostate, as well as the number of the needles to use, have been developed to help urologists perform prostate needle biopsy. The most common biopsy protocol is the systematic sextant biopsy [2]; however, recent studies have shown that this protocol produces a positive predictive value of only 20–30% [3] which results in a significant number of prostate-cancer cases being undetected at initial biopsy.