Prognostic value of adaptive textural features – The effect of standardizing nuclear first-order gray level statistics and mixing information from nuclei having different area
Affiliations: Department of Medical Informatics, The Norwegian Radium Hospital, Montebello, N-0310 Oslo, Norway | Division of Genomic Medicine, University of Sheffield, Sheffield S102TN, England
Note: [] Corresponding author: Dr. B. Nielsen, Department of Medical Informatics, The Norwegian Radium Hospital, Montebello, N-0310 Oslo, Norway. Tel.: +47 22 93 48 62; Fax: +47 22 93 56 27; E-mail: [email protected].
Abstract: Background: Nuclear texture analysis is a useful method to obtain quantitative information for use in prognosis of cancer. The first-order gray level statistics of a digitized light microscopic nuclear image may be influenced by variations in the image input conditions. Therefore, we have previously standardized the nuclear gray level mean value and standard deviation. However, there is a clear relation between nuclear DNA content, area, first-order statistics, and texture. For nuclei with approximately the same DNA content, the mean gray level increases with an increasing nuclear area. The aims of the present methodical work were to study: (1) whether the prognostic value of adaptive textural features varies with nuclear area, and (2) the effect of standardizing nuclear first-order statistics. Methods: Nuclei from 134 cases of ovarian cancer were grouped into intervals according to nuclear area. Adaptive features were extracted from two different image sets, i.e., standardized and non-standardized nuclear images. Results: The prognostic value of adaptive textural features varied strongly with nuclear area. A standardization of the first-order statistics significantly reduced this prognostic information. Several single features discriminated the two classes of cancer with a correct classification rate of 70%. Conclusion: Nuclei having an area between 2000–4999 pixels contained most of the class distance information between the good and poor prognosis classes of cancer. By considering the relation between nuclear area and texture, we avoided a loss of information caused by standardizing the first-order statistics and mixing data from cells having different nuclear area.
Keywords: Nuclear texture analysis, early ovarian cancer, prognostic classification, nuclear area, DNA content, nuclear first-order gray level statistics, nuclear area dependent class distance matrices, adaptive features
