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Analytical Cellular Pathology
Volume 35, Issue 4, Pages 305-314
http://dx.doi.org/10.3233/ACP-2012-0065

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

Birgitte Nielsen,1,3 Fritz Albregtsen,1,2 Wanja Kildal,1,3 Vera M. Abeler,4 Gunnar B. Kristensen,1,5 and Håvard E. Danielsen1,2,3

1Institute for Medical Informatics, Oslo University Hospital, Oslo, Norway
2Department of Informatics, University of Oslo, Oslo, Norway
3Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway
4Department of Pathology, Oslo University Hospital, Oslo, Norway
5Department of Gynecologic Oncology, Oslo University Hospital, Oslo, Norway

Received 10 February 2012; Accepted 25 April 2012

Copyright © 2012 Hindawi Publishing Corporation and the authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

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.