In this paper, a method for maximizing the probability of prostate cancer detection via biopsy is presented, by combining image analysis and optimization techniques. This method consists of three major steps. First, a statistical atlas of the spatial distribution of prostate cancer is constructed from histological images obtained from radical prostatectomy specimen. Second, a probabilistic optimization framework is employed to optimize the biopsy strategy, so that the probability of cancer detection is maximized under needle placement uncertainties. Finally, the optimized biopsy strategy generated in the atlas space is mapped to a specific patient space using an automated segmentation and elastic registration method. Cross-validation experiments showed that the predictive power of the optimized biopsy strategy for cancer detection reached the 94%-96% levels for 6-7 biopsy cores, which is significantly better than standard random-systematic biopsy protocols, thereby encouraging further investigation of optimized biopsy strategies in prospective clinical studies.
Bibliographical noteFunding Information:
Manuscript received September 20, 2006; revised December 4, 2006. This work was supported in part by the National Institutes of Health (NIH) under Grant R01 CA104976. Asterisk indicates corresponding author. *Y. Zhan is with the Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA. He is also with the Center of Computer-Integrated Surgery and the Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218 USA (e-mail: email@example.com).
- Biopsy optimization
- Prostate cancer
- Spatial normalization
- Statistical image analysis
ASJC Scopus subject areas
- Radiological and Ultrasound Technology
- Computer Science Applications
- Electrical and Electronic Engineering