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Distribution of prostate cancer for optimized biopsy protocols

Publication ,  Conference
Zeng, J; Bauer, JJ; Sofer, A; Yao, X; Opell, B; Zhang, W; Sesterhenn, IA; Moul, JW; Lynch, J; Mun, SK
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2000

Prostate cancer is the leading cause of death for American men. The gold standard for diagnosis of prostate cancer is transrectal ultrasound-guided needle core biopsy. Unfortunately, no imaging modality, including ultrasound, can effectively differentiate prostate cancer from normal tissues. As a result, most current prostate needle biopsy procedures have to be performed under empiric protocols, leading to unsatisfactory detection rate. The goal of this research is to establish an accurate 3D distribution map of prostate cancer and develop optimized biopsy protocols. First, we used real prostate specimens with localized cancer to reconstruct 3D prostate models. We then divided each model into zones based on clinical conventions, and calculate cancer presence in each zone. As a result, an accurate 3D prostate cancer distribution map was developed using 281 prostate models. Finally, the linear programming approach was used to optimize biopsy protocols using anatomy and symmetry constraints, and the optimized protocols were developed with various criteria.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783540411895

Publication Date

January 1, 2000

Volume

1935

Start / End Page

287 / 296

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Zeng, J., Bauer, J. J., Sofer, A., Yao, X., Opell, B., Zhang, W., … Mun, S. K. (2000). Distribution of prostate cancer for optimized biopsy protocols. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1935, pp. 287–296). https://doi.org/10.1007/978-3-540-40899-4_29
Zeng, J., J. J. Bauer, A. Sofer, X. Yao, B. Opell, W. Zhang, I. A. Sesterhenn, J. W. Moul, J. Lynch, and S. K. Mun. “Distribution of prostate cancer for optimized biopsy protocols.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1935:287–96, 2000. https://doi.org/10.1007/978-3-540-40899-4_29.
Zeng J, Bauer JJ, Sofer A, Yao X, Opell B, Zhang W, et al. Distribution of prostate cancer for optimized biopsy protocols. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2000. p. 287–96.
Zeng, J., et al. “Distribution of prostate cancer for optimized biopsy protocols.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1935, 2000, pp. 287–96. Scopus, doi:10.1007/978-3-540-40899-4_29.
Zeng J, Bauer JJ, Sofer A, Yao X, Opell B, Zhang W, Sesterhenn IA, Moul JW, Lynch J, Mun SK. Distribution of prostate cancer for optimized biopsy protocols. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2000. p. 287–296.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783540411895

Publication Date

January 1, 2000

Volume

1935

Start / End Page

287 / 296

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences