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Sampling the spatial patterns of cancer: optimized biopsy procedures for estimating prostate cancer volume and Gleason Score.

Publication ,  Journal Article
Ou, Y; Shen, D; Zeng, J; Sun, L; Moul, J; Davatzikos, C
Published in: Med Image Anal
August 2009

Prostate biopsy is the current gold-standard procedure for prostate cancer diagnosis. Existing prostate biopsy procedures have been mostly focusing on detecting cancer presence. However, they often ignore the potential use of biopsy to estimate cancer volume (CV) and Gleason Score (GS, a cancer grade descriptor), the two surrogate markers for cancer aggressiveness and the two crucial factors for treatment planning. To fill up this vacancy, this paper assumes and demonstrates that, by optimally sampling the spatial patterns of cancer, biopsy procedures can be specifically designed for estimating CV and GS. Our approach combines image analysis and machine learning tools in an atlas-based population study that consists of three steps. First, the spatial distributions of cancer in a patient population are learned, by constructing statistical atlases from histological images of prostate specimens with known cancer ground truths. Then, the optimal biopsy locations are determined in a feature selection formulation, so that biopsy outcomes (either cancer presence or absence) at those locations could be used to differentiate, at the best rate, between the existing specimens having different (high vs. low) CV/GS values. Finally, the optimized biopsy locations are utilized to estimate whether a new-coming prostate cancer patient has high or low CV/GS values, based on a binary classification formulation. The estimation accuracy and the generalization ability are evaluated by the classification rates and the associated receiver-operating-characteristic (ROC) curves in cross validations. The optimized biopsy procedures are also designed to be robust to the almost inevitable needle displacement errors in clinical practice, and are found to be robust to variations in the optimization parameters as well as the training populations.

Duke Scholars

Published In

Med Image Anal

DOI

EISSN

1361-8423

Publication Date

August 2009

Volume

13

Issue

4

Start / End Page

609 / 620

Location

Netherlands

Related Subject Headings

  • Sensitivity and Specificity
  • Reproducibility of Results
  • Prostatic Neoplasms
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Male
  • Imaging, Three-Dimensional
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Humans
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Ou, Y., Shen, D., Zeng, J., Sun, L., Moul, J., & Davatzikos, C. (2009). Sampling the spatial patterns of cancer: optimized biopsy procedures for estimating prostate cancer volume and Gleason Score. Med Image Anal, 13(4), 609–620. https://doi.org/10.1016/j.media.2009.05.002
Ou, Yangming, Dinggang Shen, Jianchao Zeng, Leon Sun, Judd Moul, and Christos Davatzikos. “Sampling the spatial patterns of cancer: optimized biopsy procedures for estimating prostate cancer volume and Gleason Score.Med Image Anal 13, no. 4 (August 2009): 609–20. https://doi.org/10.1016/j.media.2009.05.002.
Ou Y, Shen D, Zeng J, Sun L, Moul J, Davatzikos C. Sampling the spatial patterns of cancer: optimized biopsy procedures for estimating prostate cancer volume and Gleason Score. Med Image Anal. 2009 Aug;13(4):609–20.
Ou, Yangming, et al. “Sampling the spatial patterns of cancer: optimized biopsy procedures for estimating prostate cancer volume and Gleason Score.Med Image Anal, vol. 13, no. 4, Aug. 2009, pp. 609–20. Pubmed, doi:10.1016/j.media.2009.05.002.
Ou Y, Shen D, Zeng J, Sun L, Moul J, Davatzikos C. Sampling the spatial patterns of cancer: optimized biopsy procedures for estimating prostate cancer volume and Gleason Score. Med Image Anal. 2009 Aug;13(4):609–620.
Journal cover image

Published In

Med Image Anal

DOI

EISSN

1361-8423

Publication Date

August 2009

Volume

13

Issue

4

Start / End Page

609 / 620

Location

Netherlands

Related Subject Headings

  • Sensitivity and Specificity
  • Reproducibility of Results
  • Prostatic Neoplasms
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Male
  • Imaging, Three-Dimensional
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Humans