Creating a Statistical Atlas of Prostate Cancer for Guiding Prostate Biopsy
Prostate cancer is one of the leading causes of death in men. Early diagnosis of cancer, and especially of clinically significant cancer, is critical for effective treatment. Current imaging methods are insufficient for accurate detection of prostate cancer; therefore definitive diagnosis is typically achieved via biopsy. Current biopsy approaches place 6 or more needles into the prostate and extract tissue samples, and are known to miss a large number of cancers due to simply sampling error. We have investigated systematic (targeted) biopsy methods that obtain tissue samples from locations that, together, maximize the probability of detecting cancer . We have utilized a database of 158 radical prostatectomy specimens processed at the Center for Prostate Disease Research, and developed a statistical atlas of the spatial distribution of prostate cancer; this process required the application of elastic-type deformable registration techniques, as explained in  and the documentation. We subsequently applied optimization methods and determined the optimal needle placement that maximized probability of cancer detection for unconstrained, transrectal and transperineal biopsies. We are currently working on adding multi-parametric MRI segmentation and pattern classification methods, in order to combine population-based data with patient-specific information towards a patient-optimized biopsy system. The atlas of cancer distribution, as well as the optimized biopsy plans, can be obtained here.
A subject warped to the model. Overlay of the prostate boundary of the subject (red) after deformable registration with the template prostate (white).
The middle cross-sections of ten prostate subjects, before and after normalization. The red regions denote prostate cancer.
Optimal biopsy strategy using statistical atlas of cancer distribution. We tested our needle optimization method on 100 subjects. In the following figure, the optimal biopsy sites are shown as white spheres and the prostate capsule is shown as red. The underlying spatial statistical distribution of cancer inside of prostate capsule is shown in green. Brighter green indicates higher likelihood of finding cancer in that location. In this case, seven needles were adequate to detect the tumor correctly in all 100 subjects. In particular, the first five needles can detect the tumor with an accuracy of 97%. An important implication is that the optimized needle placement is not necessarily on regions that have high likelihood of cancer. As we can see from the following figure, only first three white needles were placed in brighter green (high likelihood) regions. The remaining four were placed in regions that were almost statistically independent from the first three.
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