The prognostic value of adaptive nuclear texture features from patient gray level entropy matrices in early stage ovarian cancer.

Background: Nuclear texture analysis gives information about the spatial arrangement of the pixel gray levels in a digitized microscopic nuclear image, providing texture features that may be used as quantitative tools for prognosis of human cancer. The aim of the study was to evaluate the prognostic value of adaptive nuclear texture features in early stage ovarian cancer.
Methods: 246 cases of early stage ovarian cancer were included in the analysis. Isolated nuclei (monolayers) were prepared from 50 μm tissue sections and stained with Feulgen-Schiff. Local gray level entropy was measured within small windows of each nuclear image and stored in gray level entropy matrices. A compact set of adaptive features was computed from these matrices.
Results: Univariate Kaplan-Meier analysis showed significantly better relapse-free survival (p < 0.001) for patients with low adaptive feature values compared to patients with high adaptive feature values. The 10-year relapse-free survival was about 78% for patients with low feature values and about 52% for patients with high feature values. Adaptive features were found to be of independent prognostic significance for relapse-free survival in a multivariate analysis.
Conclusion: Adaptive nuclear texture features from entropy matrices contain prognostic information and are of independent prognostic significance for relapse-free survival in early stage ovarian cancer.