TY - JOUR
T1 - Targeted prostate biopsy using statistical image analysis
AU - Zhan, Yiqiang
AU - Shen, Dinggang
AU - Zeng, Jianchao
AU - Sun, Leon
AU - Fichtinger, Gabor
AU - Moul, Judd
AU - Davatzikos, Christos
N1 - Funding 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: yzhan@cs.jhu.edu).
PY - 2007/6
Y1 - 2007/6
N2 - 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.
AB - 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.
KW - Biopsy optimization
KW - Prostate cancer
KW - Spatial normalization
KW - Statistical image analysis
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U2 - 10.1109/TMI.2006.891497
DO - 10.1109/TMI.2006.891497
M3 - Article
C2 - 17679329
AN - SCOPUS:34249737596
SN - 0278-0062
VL - 26
SP - 779
EP - 788
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 6
ER -