Statistically optimized biopsy strategy for the diagnosis of prostate cancer

Dinggang Shen, Zhiqiang Lao, Jianchao Zeng, Edward H. Herskovits, Gabor Fichtinger, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


This paper 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, the results on the accuracy of deformable registration and the predictive power of our statistically optimized biopsy strategy are presented in this paper.

Original languageEnglish
Pages (from-to)433-438
Number of pages6
JournalProceedings of the IEEE Symposium on Computer-Based Medical Systems
Publication statusPublished - 2001
Externally publishedYes

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications


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