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 o ptical texture measurement and analysis of gray-scale images. The authors, utilizing image analysis, prospectively studied Markov nuclear texture feat ures to determine their relevance as prognostic indicators of survival in p atients with epithelial ovarian carcinoma. Ninety-nine consecutive patients with ovarian cancer, treated initially wit h surgery were evaluated for their length of survival, level of cytoreducti on, FIGO stage, grade, histology, and DNA index, as well as 20 Markov textu re features. Markov nuclear texture features were quantified using image an alysis. Mean follow-up for the study population was 64 months (median 59) with a ra nge from 51 to 89 months. Five optical texture features showed significant correlation with length of survival. Difference entropy (P = 0.033) and inf ormation measure A (P = 0.041) were both indirectly correlated with surviva l while information measure B (P = 0.030), correlation coefficient (P = 0.0 45), and the maximum correlation coefficient (P = 0.041) were directly corr elated. 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.