Optimized prostate biopsy via a statistical atlas of cancer spatial distribution

✍ Scribed by Dinggang Shen; Zhiqiang Lao; Jianchao Zeng; Wei Zhang; Isabel A. Sesterhenn; Leon Sun; Judd W. Moul; Edward H. Herskovits; Gabor Fichtinger; Christos Davatzikos

• Publisher: Elsevier Science
• Year: 2004
• Tongue: English
• Weight: 661 KB
• Volume: 8
• Category: Article
• ISSN: 1361-8415
• DOI: 10.1016/j.media.2003.11.002

✦ Synopsis

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.