Literature DB >> 12496982

Investigating the distribution of prostate cancer using three-dimensional computer simulation.

M B Opell1, J Zeng, J J Bauer, R R Connelly, W Zhang, I A Sesterhenn, S K Mun, J W Moul, J H Lynch.   

Abstract

The objective of this work was to investigate the distribution of prostate cancer using three-dimensional (3-D) computer simulation. Two hundred and eighty-one 3-D computer prostate models were constructed from radical prostatectomy specimens. An algorithm was developed which divided each model into 24 symmetrical regions, and it then detected the presence of tumor within an individual region. The distribution rate of prostate cancer was assessed within each region of all 281 prostate models, and the difference between the rates was statistically analyzed using Mantel-Haenszel methodology. There was a statistically significant higher distribution rate of cancer in the posterior half (57.2%) compared to the anterior half ( 40.5%; P=0.001). The base regions (36.8%) had a statistically significant lower distribution rate than either the mid regions (56.3%; P=0.001) or the apical regions (53.5%; P=0.001). The mid regions did have a statistically significant higher distribution rate compared to the apical regions (P=0.032). There was no statistically significant difference between the distribution rate on the left half (48.5%) compared to that on the right half (49.2%; P=0.494). The spatial distribution of prostate cancer can be analyzed using 3-D computer prostate models. The results illustrate that prostate cancer is least commonly located in the anterior half and base regions of the prostate. Through an analysis of the spatial distribution of prostate cancer, we believe that new optimal biopsy strategies and techniques can be developed.

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Year:  2002        PMID: 12496982     DOI: 10.1038/sj.pcan.4500577

Source DB:  PubMed          Journal:  Prostate Cancer Prostatic Dis        ISSN: 1365-7852            Impact factor:   5.554


  6 in total

1.  Anterior tumors of the prostate: diagnosis and significance.

Authors:  Priya N Werahera; E David Crawford; Francisco G La Rosa; Kathleen C Torkko; Beth Schulte; Holly T Sullivan; Adrie van Bokhoven; M Scott Lucia; Fernando J Kim
Journal:  Can J Urol       Date:  2013-10       Impact factor: 1.344

2.  Generating prostate models by means of geometric deformation with application to computerized training of cryosurgery.

Authors:  Anjali Sehrawat; Kenji Shimada; Yoed Rabin
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-07-11       Impact factor: 2.924

3.  Transperineal prostate biopsy: analysis of a uniform core sampling pattern that yields data on tumor volume limits in negative biopsies.

Authors:  Gordon R Kepner; Jeremy V Kepner
Journal:  Theor Biol Med Model       Date:  2010-06-17       Impact factor: 2.432

Review 4.  Three-dimensional sonography with needle tracking: role in diagnosis and treatment of prostate cancer.

Authors:  Feimo Shen; Katsuto Shinohara; Dinesh Kumar; Animesh Khemka; Anne R Simoneau; Priya N Werahera; Lu Li; Yujun Guo; Ramkrishnan Narayanan; Liyang Wei; Al Barqawi; E David Crawford; Christos Davatzikos; Jasjit S Suri
Journal:  J Ultrasound Med       Date:  2008-06       Impact factor: 2.153

5.  Methodology to study the three-dimensional spatial distribution of prostate cancer and their dependence on clinical parameters.

Authors:  Kristians Diaz Rojas; Maria L Montero; Jorge Yao; Edward Messing; Anees Fazili; Jean Joseph; Yangming Ou; Deborah J Rubens; Kevin J Parker; Christos Davatzikos; Benjamin Castaneda
Journal:  J Med Imaging (Bellingham)       Date:  2015-07-29

6.  Three-Dimensional Presentation of Tumor Histopathology: A Model Using Tongue Squamous Cell Carcinoma.

Authors:  Anne Koivuholma; Katri Aro; Antti Mäkitie; Mika Salmi; Tuomas Mirtti; Jaana Hagström; Timo Atula
Journal:  Diagnostics (Basel)       Date:  2021-01-12
  6 in total

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