A methodology to study the relationship between clinical variables [e.g., prostate specific antigen (PSA) or Gleason score] and cancer spatial distribution is described.
FREE DAILY AND WEEKLY NEWSLETTERS OFFERED BY CONTENT OF INTEREST
Did you find this article relevant? Subscribe to UroToday-GUOncToday!
The fields of GU Oncology and Urology are advancing rapidly including new treatments, enrolling clinical trials, screening and surveillance recommendations along with updated guidelines. Join us as one of our subscribers who rely on UroToday as their must-read source for the latest news and data on drugs. Sign up today for blogs, video conversations, conference highlights and abstracts from peer-review publications by disease and condition delivered to your inbox and read on the go.
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.
J Med Imaging (Bellingham). 2015 Jul;2(3):037502. doi: 10.1117/1.JMI.2.3.037502. Epub 2015 Jul 29.
Rojas KD1, Montero ML2, Yao J3, Messing E4, Fazili A4, Joseph J4, Ou Y5, Rubens DJ6, Parker KJ7, Davatzikos C8, Castaneda B1.
1 Pontificia Universidad Católica del Perú , Department of Engineering, Section in Electrical and Electronic, Laboratory Medical Images, Av. Universitaria 1801, San Miguel Lima 32, Perú
2 Pontificia Universidad Católica del Perú , Department of Science, Section of Mathematics, Laboratory Statistics, Av. Universitaria 1801, San Miguel Lima 32, Perú
3 University of Rochester Medical Center , Department of Pathology and Laboratory Medicine, 601 Elmwood Avenue, Box 648, Rochester, New York 14642, United States.
4 University of Rochester Medical Center , Department of Urology, 601 Elmwood Avenue, Box 648, Rochester, New York 14642, United States.
5 Athinoula A. Martinos Center for Biomedical Imaging , Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts 02129, United States.
6 University of Rochester Medical Center , Department of Imaging Sciences, 601 Elmwood Avenue, Box 648, Rochester, New York 14642, United States.
7 University of Rochester , Department of Electrical and Computer Engineering, Hopeman Engineering Building 203, Box 270126, Rochester, New York 14627, United States.
8University of Pennsylvania , Departments of Radiology and Electrical and Computer Engineering, 3600 Market Street, Suite 380, Philadelphia, Pennsylvania 19104, United States.