The Prognostic Value of Adaptive Nuclear Texture Features from Patient Gray Level Entropy Matrices in Early Stage Ovarian Cancer

Abstract

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.