Targeted prostate biopsy using statistical image analysis

Yiqiang Zhan; Dinggang Shen; Jianchao Zeng; Leon Sun; Gabor Fichtinger; Judd Moul; Christos Davatzikos

doi:10.1109/TMI.2006.891497

Targeted prostate biopsy using statistical image analysis

Yiqiang Zhan, Dinggang Shen, Jianchao Zeng, Leon Sun, Gabor Fichtinger, Judd Moul, Christos Davatzikos

Research output: Contribution to journal › Article › peer-review

51 Citations (Scopus)

Abstract

In this paper, a method for maximizing the probability of prostate cancer detection via biopsy is presented, by combining image analysis and optimization techniques. This method consists of three major steps. First, a statistical atlas of the spatial distribution of prostate cancer is constructed from histological images obtained from radical prostatectomy specimen. Second, a probabilistic optimization framework is employed to optimize the biopsy strategy, so that the probability of cancer detection is maximized under needle placement uncertainties. Finally, the optimized biopsy strategy generated in the atlas space is mapped to a specific patient space using an automated segmentation and elastic registration method. Cross-validation experiments showed that the predictive power of the optimized biopsy strategy for cancer detection reached the 94%-96% levels for 6-7 biopsy cores, which is significantly better than standard random-systematic biopsy protocols, thereby encouraging further investigation of optimized biopsy strategies in prospective clinical studies.

Original language	English
Pages (from-to)	779-788
Number of pages	10
Journal	IEEE Transactions on Medical Imaging
Volume	26
Issue number	6
DOIs	https://doi.org/10.1109/TMI.2006.891497
Publication status	Published - 2007 Jun

Bibliographical note

Funding Information:
Manuscript received September 20, 2006; revised December 4, 2006. This work was supported in part by the National Institutes of Health (NIH) under Grant R01 CA104976. Asterisk indicates corresponding author. *Y. Zhan is with the Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA. He is also with the Center of Computer-Integrated Surgery and the Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218 USA (e-mail: [email protected]).

Keywords

Biopsy optimization
Prostate cancer
Spatial normalization
Statistical image analysis

ASJC Scopus subject areas

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TMI.2006.891497

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title = "Targeted prostate biopsy using statistical image analysis",

abstract = "In this paper, a method for maximizing the probability of prostate cancer detection via biopsy is presented, by combining image analysis and optimization techniques. This method consists of three major steps. First, a statistical atlas of the spatial distribution of prostate cancer is constructed from histological images obtained from radical prostatectomy specimen. Second, a probabilistic optimization framework is employed to optimize the biopsy strategy, so that the probability of cancer detection is maximized under needle placement uncertainties. Finally, the optimized biopsy strategy generated in the atlas space is mapped to a specific patient space using an automated segmentation and elastic registration method. Cross-validation experiments showed that the predictive power of the optimized biopsy strategy for cancer detection reached the 94%-96% levels for 6-7 biopsy cores, which is significantly better than standard random-systematic biopsy protocols, thereby encouraging further investigation of optimized biopsy strategies in prospective clinical studies.",

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author = "Yiqiang Zhan and Dinggang Shen and Jianchao Zeng and Leon Sun and Gabor Fichtinger and Judd Moul and Christos Davatzikos",

note = "Funding Information: Manuscript received September 20, 2006; revised December 4, 2006. This work was supported in part by the National Institutes of Health (NIH) under Grant R01 CA104976. Asterisk indicates corresponding author. *Y. Zhan is with the Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA. He is also with the Center of Computer-Integrated Surgery and the Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218 USA (e-mail: [email protected]).",

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doi = "10.1109/TMI.2006.891497",

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AU - Zhan, Yiqiang

AU - Shen, Dinggang

AU - Zeng, Jianchao

AU - Sun, Leon

AU - Fichtinger, Gabor

AU - Moul, Judd

AU - Davatzikos, Christos

N1 - Funding Information: Manuscript received September 20, 2006; revised December 4, 2006. This work was supported in part by the National Institutes of Health (NIH) under Grant R01 CA104976. Asterisk indicates corresponding author. *Y. Zhan is with the Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA. He is also with the Center of Computer-Integrated Surgery and the Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218 USA (e-mail: [email protected]).

PY - 2007/6

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N2 - In this paper, a method for maximizing the probability of prostate cancer detection via biopsy is presented, by combining image analysis and optimization techniques. This method consists of three major steps. First, a statistical atlas of the spatial distribution of prostate cancer is constructed from histological images obtained from radical prostatectomy specimen. Second, a probabilistic optimization framework is employed to optimize the biopsy strategy, so that the probability of cancer detection is maximized under needle placement uncertainties. Finally, the optimized biopsy strategy generated in the atlas space is mapped to a specific patient space using an automated segmentation and elastic registration method. Cross-validation experiments showed that the predictive power of the optimized biopsy strategy for cancer detection reached the 94%-96% levels for 6-7 biopsy cores, which is significantly better than standard random-systematic biopsy protocols, thereby encouraging further investigation of optimized biopsy strategies in prospective clinical studies.

AB - In this paper, a method for maximizing the probability of prostate cancer detection via biopsy is presented, by combining image analysis and optimization techniques. This method consists of three major steps. First, a statistical atlas of the spatial distribution of prostate cancer is constructed from histological images obtained from radical prostatectomy specimen. Second, a probabilistic optimization framework is employed to optimize the biopsy strategy, so that the probability of cancer detection is maximized under needle placement uncertainties. Finally, the optimized biopsy strategy generated in the atlas space is mapped to a specific patient space using an automated segmentation and elastic registration method. Cross-validation experiments showed that the predictive power of the optimized biopsy strategy for cancer detection reached the 94%-96% levels for 6-7 biopsy cores, which is significantly better than standard random-systematic biopsy protocols, thereby encouraging further investigation of optimized biopsy strategies in prospective clinical studies.

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KW - Spatial normalization

KW - Statistical image analysis

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Targeted prostate biopsy using statistical image analysis

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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