Optimized prostate biopsy via a statistical atlas of cancer spatial distribution.
A methodology is presented for constructing a statistical atlas of spatial distribution of prostate cancer from a large patient cohort, and it is used for optimizing needle biopsy. An adaptive-focus deformable model is used for the spatial normalization and registration of 100 prostate histological samples, which were provided by the Center for Prostate Disease Research of the US Department of Defense, resulting in a statistical atlas of spatial distribution of prostate cancer. Based on this atlas, a statistical predictive model was developed to optimize the needle biopsy sites, by maximizing the probability of detecting cancer. Experimental results using cross-validation show that the proposed method can detect cancer with a 99% success rate using seven needles, in these samples.
Shen
Dinggang
D
Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104-2644, USA. dgshen@rad.upenn.edu
Lao
Zhiqiang
Z
Zeng
Jianchao
J
Zhang
Wei
W
Sesterhenn
Isabel A
IA
Sun
Leon
L
Moul
Judd W
JW
Herskovits
Edward H
EH
Fichtinger
Gabor
G
Davatzikos
Christos
C
D000465:Algorithms; D001707:Biopsy, Needle / Q000706:statistics & numerical data; D015331:Cohort Studies; D003198:Computer Simulation; D005544:Forecasting; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D011336:Probability; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results