Quantification of fractal dimension and Shannon’s entropy in histological diagnosis of prostate cancer

BackgroundProstate cancer is a serious public health problem that affects quality of life and has a significant mortality rate. The aim of the present study was to quantify the fractal dimension and Shannon’s entropy in the histological diagnosis of prostate cancer.MethodsThirty-four patients with prostate cancer aged 50 to 75 years having been submitted to radical prostatectomy participated in the study. Histological slides of normal (N), hyperplastic (H) and tumor (T) areas of the prostate were digitally photographed with three different magnifications (40x, 100x and 400x) and analyzed. The fractal dimension (FD), Shannon’s entropy (SE) and number of cell nuclei (NCN) in these areas were compared.ResultsFD analysis demonstrated the following significant differences between groups: T vs. N and H vs. N groups (p < 0.05) at a magnification of 40x; T vs. N (p < 0.01) at 100x and H vs. N (p < 0.01) at 400x. SE analysis revealed the following significant differences groups: T vs. H and T vs. N (p < 0.05) at 100x; and T vs. H and T vs. N (p < 0.001) at 400x. NCN analysis demonstrated the following significant differences between groups: T vs. H and T vs. N (p < 0.05) at 40x; T vs. H and T vs. N (p < 0.0001) at 100x; and T vs. H and T vs. N (p < 0.01) at 400x.ConclusionsThe quantification of the FD and SE, together with the number of cell nuclei, has potential clinical applications in the histological diagnosis of prostate cancer.

[1]  P. Mousavi,et al.  Discrete Fourier Analysis of Ultrasound RF Time Series for Detection of Prostate Cancer , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Amar Partap Singh Pharwaha,et al.  Shannon and Non-Shannon Measures of Entropy for Statistical Texture Feature Extraction in Digitized Mammograms , 2009 .

[3]  K Yogesan,et al.  Entropy-based texture analysis of chromatin structure in advanced prostate cancer. , 1996, Cytometry.

[4]  Gabriel Landini,et al.  Quantification of the global and local complexity of the epithelial-connective tissue interface of normal, dysplastic, and neoplastic oral mucosae using digital imaging. , 2003, Pathology, research and practice.

[5]  A. Villers,et al.  Prostate cancer characterization on MR images using fractal features. , 2010, Medical physics.

[6]  G. Gillon,et al.  PSA levels of 4.0 - 10 ng/mL and negative digital rectal examination. Antibiotic therapy versus immediate prostate biopsy. , 2009, International braz j urol : official journal of the Brazilian Society of Urology.

[7]  M Keipes,et al.  Of the British coastline and the interest of fractals in medicine. , 1993, Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie.

[8]  P. Humphrey,et al.  The early detection of prostate carcinoma with prostate specific antigen , 1997, Cancer.

[9]  Alexei Kouznetsov,et al.  Quantifying the architectural complexity of microscopic images of histology specimens. , 2009, Micron.

[10]  R. Simeonov,et al.  Fractal dimension of canine mammary gland epithelial tumors on cytologic smears. , 2006, Veterinary clinical pathology.

[11]  Rosalie Nolley,et al.  The prostate specific antigen era in the United States is over for prostate cancer: what happened in the last 20 years? , 2004, The Journal of urology.

[12]  B. G. Blijenberg,et al.  Screening and prostate-cancer mortality in a randomized European study. , 2009, The New England journal of medicine.

[13]  Barbara Franceschini,et al.  Fractal analysis of two-dimensional vascularity in primary prostate cancer and surrounding non-tumoral parenchyma. , 2009, Pathology, research and practice.