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

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 5  ng/ml 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 5  ng/ml. 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.

[1]  Louis R Kavoussi,et al.  Accuracy of digital rectal examination and transrectal ultrasonography in localizing prostate cancer. , 1994, The Journal of urology.

[2]  Nira Dyn,et al.  Reconstruction of 3D objects from 2D cross-sections with the 4-point subdivision scheme adapted to sets , 2011, Comput. Graph..

[3]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[4]  P. J. van der Maas,et al.  Evaluation of the digital rectal examination as a screening test for prostate cancer. Rotterdam section of the European Randomized Study of Screening for Prostate Cancer. , 1999, Journal of the National Cancer Institute.

[5]  S K Mun,et al.  Prostate biopsy protocols: 3D visualization-based evaluation and clinical correlation. , 2001, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[6]  Tong-Yee Lee,et al.  Morphology-based Three-dimensional Interpolation , 2000, IEEE Trans. Medical Imaging.

[7]  Seong Ki Mun,et al.  Modeling and mapping of prostate cancer , 2000, Comput. Graph..

[8]  Dinggang Shen,et al.  Sampling the spatial patterns of cancer: Optimized biopsy procedures for estimating prostate cancer volume and Gleason Score , 2009, Medical Image Anal..

[9]  Maliha S. Nash,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 2001, Technometrics.

[10]  J. Udupa,et al.  Shape-based interpolation of multidimensional objects. , 1990, IEEE transactions on medical imaging.

[11]  T. Jang,et al.  Low risk prostate cancer in men under age 65: the case for definitive treatment. , 2007, Urologic oncology.

[12]  Max A. Viergever,et al.  A discrete dynamic contour model , 1995, IEEE Trans. Medical Imaging.

[13]  C Busch,et al.  Modeling prostate cancer distributions. , 1999, Urology.

[14]  Kevin J Parker,et al.  Three-dimensional registration of prostate images from histology and ultrasound. , 2004, Ultrasound in medicine & biology.

[15]  R. H. Myers,et al.  STAT 319 : Probability & Statistics for Engineers & Scientists Term 152 ( 1 ) Final Exam Wednesday 11 / 05 / 2016 8 : 00 – 10 : 30 AM , 2016 .

[16]  Wei Zhang,et al.  Distribution of Prostrate Cancer for Optimized Biopsy Protocols , 2000, MICCAI.

[17]  U. G. Dailey Cancer,Facts and Figures about. , 2022, Journal of the National Medical Association.

[18]  D. Shen,et al.  Registering histological and MR images of prostate for image-based cancer detection. , 2007, Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention.

[19]  Terry S. Yoo,et al.  Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis , 2004 .

[20]  E. Metter,et al.  Expectant management of prostate cancer with curative intent: an update of the Johns Hopkins experience. , 2007, The Journal of urology.

[21]  Alexandra Branzan Albu,et al.  A Morphology-Based Approach for Interslice Interpolation of Anatomical Slices From Volumetric Images , 2008, IEEE Transactions on Biomedical Engineering.

[22]  S K Mun,et al.  Three-dimensional computer-simulated prostate models: lateral prostate biopsies increase the detection rate of prostate cancer. , 1999, Urology.

[23]  Andrew B Rosenkrantz,et al.  Optimization of prostate biopsy: the role of magnetic resonance imaging targeted biopsy in detection, localization and risk assessment. , 2014, The Journal of urology.

[24]  Carolyn A. Bucholtz,et al.  Shape-based interpolation , 1992, IEEE Computer Graphics and Applications.

[25]  Daniel Rueckert,et al.  Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II , 2017, Lecture Notes in Computer Science.

[26]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[27]  Dinggang Shen,et al.  An Adaptive-Focus Deformable Model Using Statistical and Geometric Information , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Dinggang Shen,et al.  Optimized prostate biopsy via a statistical atlas of cancer spatial distribution , 2004, Medical Image Anal..

[29]  M. Graefen,et al.  Pathological and clinical characteristics of large prostate cancers predominantly located in the transition zone , 2002, Prostate Cancer and Prostatic Diseases.

[30]  M. B. Opell,et al.  Investigating the distribution of prostate cancer using three-dimensional computer simulation , 2002, Prostate Cancer and Prostatic Diseases.

[31]  C. Mathers,et al.  Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008 , 2010, International journal of cancer.

[32]  Dinggang Shen,et al.  An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures , 2001, IEEE Transactions on Medical Imaging.

[33]  Dinggang Shen,et al.  Statistically optimized biopsy strategy for the diagnosis of prostate cancer , 2001, Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001.

[34]  Dinggang Shen,et al.  Optimized biopsy procedures for estimating Gleason Score and prostate cancer volume , 2008 .

[35]  Dinggang Shen,et al.  A Statistical Atlas of Prostate Cancer for Optimal Biopsy , 2001, MICCAI.

[36]  J. Gohagan,et al.  Prostate cancer screening in the randomized Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial: mortality results after 13 years of follow-up. , 2012, Journal of the National Cancer Institute.

[37]  L. Klotz Active surveillance for prostate cancer: for whom? , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[38]  Thaddeus Beier,et al.  Feature-based image metamorphosis , 1998 .

[39]  T. Stamey,et al.  Zonal Distribution of Prostatic Adenocarcinoma: Correlation with Histologic Pattern and Direction of Spread , 1988, The American journal of surgical pathology.