Markov optical texture parameters as prognostic indicators in ovarian carcinoma.
Texture is a descriptive property of a surface describing the morphometric heterogeneity of complex structures. Computer aided image analysis allows optical texture measurement and analysis of gray-scale images. The authors, utilizing image analysis, prospectively studied Markov nuclear texture features to determine their relevance as prognostic indicators of survival in patients with epithelial ovarian carcinoma. Ninety-nine consecutive patients with ovarian cancer, treated initially with surgery were evaluated for their length of survival, level of cytoreduction, FIGO stage, grade, histology, and DNA index, as well as 20 Markov texture features. Markov nuclear texture features were quantified using image analysis. Mean follow-up for the study population was 64 months (median 59) with a range from 51 to 89 months. Five optical texture features showed significant correlation with length of survival. Difference entropy (P = 0.033) and information measure A (P = 0.041) were both indirectly correlated with survival while information measure B (P = 0.030), correlation coefficient (P = 0.045), and the maximum correlation coefficient (P = 0.041) were directly correlated. Only sum entropy (P = 0.035), FIGO stage (P = 0.0031), and level of cytoreduction (P < 0.0001) were independent predictors of survival in this population. Optical texture can be quantified by image analysis. Utilizing multivariate analysis, the Markov texture feature, sum entropy, was demonstrated to be an independent prognostic indicator of survival in patients with epithelial ovarian cancer. FIGO stage and optimal cytoreduction also were independent prognostic indicators of survival.
Division of Gynecologic Oncology, Department of Obstetrics and Gynecology; Division of Oncology Research, Department of Medicine, St. Vincent Hospitals and Health Services and Department of Pathology, Laboratory for Diagnostic and Analytical Cytometry, Indianapolis, Indiana.
J. P.
JP
Geisler
H.E.
HE
Geisler
G.A.
GA
Miller
M.C.
MC
Wiemann
Z.
Z
Zhou
W.
W
Crabtree