Statistically optimized biopsy strategy for the diagnosis of prostate cancer

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

doi:10.1109/CBMS.2001.941758

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 journal › Article › peer-review

4 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	433-438
Number of pages	6
Journal	Proceedings of the IEEE Symposium on Computer-Based Medical Systems
DOIs	https://doi.org/10.1109/CBMS.2001.941758
Publication status	Published - 2001
Externally published	Yes

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Computer Science Applications

UN SDGs

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

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10.1109/CBMS.2001.941758

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title = "Statistically optimized biopsy strategy for the diagnosis of prostate cancer",

abstract = "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.",

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T1 - Statistically optimized biopsy strategy for the diagnosis of prostate cancer

AU - Shen, Dinggang

AU - Lao, Zhiqiang

AU - Zeng, Jianchao

AU - Herskovits, Edward H.

AU - Fichtinger, Gabor

AU - Davatzikos, Christos

PY - 2001

Y1 - 2001

N2 - 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.

AB - 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.

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Statistically optimized biopsy strategy for the diagnosis of prostate cancer

Abstract

ASJC Scopus subject areas

UN SDGs

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