Literature DB >> 26236756

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

Kristians Diaz Rojas1, Maria L Montero2, Jorge Yao3, Edward Messing4, Anees Fazili4, Jean Joseph4, Yangming Ou5, Deborah J Rubens6, Kevin J Parker7, Christos Davatzikos8, Benjamin Castaneda1.   

Abstract

A methodology to study the relationship between clinical variables [e.g., prostate specific antigen (PSA) or Gleason score] and cancer spatial distribution is described. Three-dimensional (3-D) models of 216 glands are reconstructed from digital images of whole mount histopathological slices. The models are deformed into one prostate model selected as an atlas using a combination of rigid, affine, and B-spline deformable registration techniques. Spatial cancer distribution is assessed by counting the number of tumor occurrences among all glands in a given position of the 3-D registered atlas. Finally, a difference between proportions is used to compare different spatial distributions. As a proof of concept, we compare spatial distributions from patients with PSA greater and less than [Formula: see text] and from patients older and younger than 60 years. Results suggest that prostate cancer has a significant difference in the right zone of the prostate between populations with PSA greater and less than [Formula: see text]. Age does not have any impact in the spatial distribution of the disease. The proposed methodology can help to comprehend prostate cancer by understanding its spatial distribution and how it changes according to clinical parameters. Finally, this methodology can be easily adapted to other organs and pathologies.

Entities:  

Keywords:  image processing; prostate cancer; prostate specific antigen; registration; spatial distribution; ultrasound

Year:  2015        PMID: 26236756      PMCID: PMC4518233          DOI: 10.1117/1.JMI.2.3.037502

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  22 in total

1.  Three-dimensional computer-simulated prostate models: lateral prostate biopsies increase the detection rate of prostate cancer.

Authors:  J J Bauer; J Zeng; J Weir; W Zhang; I A Sesterhenn; R R Connelly; S K Mun; J W Moul
Journal:  Urology       Date:  1999-05       Impact factor: 2.649

2.  Morphology-based three-dimensional interpolation.

Authors:  T Y Lee; W H Wang
Journal:  IEEE Trans Med Imaging       Date:  2000-07       Impact factor: 10.048

3.  An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures.

Authors:  D Shen; E H Herskovits; C Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2001-04       Impact factor: 10.048

4.  Optimized prostate biopsy via a statistical atlas of cancer spatial distribution.

Authors:  Dinggang Shen; Zhiqiang Lao; Jianchao Zeng; Wei Zhang; Isabel A Sesterhenn; Leon Sun; Judd W Moul; Edward H Herskovits; Gabor Fichtinger; Christos Davatzikos
Journal:  Med Image Anal       Date:  2004-06       Impact factor: 8.545

5.  Shape-based interpolation of multidimensional objects.

Authors:  S P Raya; J K Udupa
Journal:  IEEE Trans Med Imaging       Date:  1990       Impact factor: 10.048

Review 6.  Active surveillance for prostate cancer: for whom?

Authors:  Laurence Klotz
Journal:  J Clin Oncol       Date:  2005-11-10       Impact factor: 44.544

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

Authors:  M B Opell; J Zeng; J J Bauer; R R Connelly; W Zhang; I A Sesterhenn; S K Mun; J W Moul; J H Lynch
Journal:  Prostate Cancer Prostatic Dis       Date:  2002       Impact factor: 5.554

Review 8.  Low risk prostate cancer in men under age 65: the case for definitive treatment.

Authors:  Thomas L Jang; Ofer Yossepowitch; Fernando J Bianco; Peter T Scardino
Journal:  Urol Oncol       Date:  2007 Nov-Dec       Impact factor: 3.498

9.  Zonal distribution of prostatic adenocarcinoma. Correlation with histologic pattern and direction of spread.

Authors:  J E McNeal; E A Redwine; F S Freiha; T A Stamey
Journal:  Am J Surg Pathol       Date:  1988-12       Impact factor: 6.394

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

Authors:  Yangming Ou; Dinggang Shen; Jianchao Zeng; Leon Sun; Judd Moul; Christos Davatzikos
Journal:  Med Image Anal       Date:  2009-05-23       Impact factor: 8.545

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  3 in total

Review 1.  Accurate validation of ultrasound imaging of prostate cancer: a review of challenges in registration of imaging and histopathology.

Authors:  Rogier R Wildeboer; Ruud J G van Sloun; Arnoud W Postema; Christophe K Mannaerts; Maudy Gayet; Harrie P Beerlage; Hessel Wijkstra; Massimo Mischi
Journal:  J Ultrasound       Date:  2018-07-30

2.  Comparative analysis of tissue reconstruction algorithms for 3D histology.

Authors:  Kimmo Kartasalo; Leena Latonen; Jorma Vihinen; Tapio Visakorpi; Matti Nykter; Pekka Ruusuvuori
Journal:  Bioinformatics       Date:  2018-09-01       Impact factor: 6.937

3.  A statistical, voxelised model of prostate cancer for biologically optimised radiotherapy.

Authors:  Robert N Finnegan; Hayley M Reynolds; Martin A Ebert; Yu Sun; Lois Holloway; Jonathan R Sykes; Jason Dowling; Catherine Mitchell; Scott G Williams; Declan G Murphy; Annette Haworth
Journal:  Phys Imaging Radiat Oncol       Date:  2022-03-06
  3 in total

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