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.
Highlights
Nuclear texture analysis is a useful method to obtain quantitative information for the diagnosis and prognosis of human cancer [6,9,12,14,15,26], and gives information about the spatial arrangement of the pixel gray levels in a digitized microscopic nuclear image
The first-order gray level statistics of an image are affected by the image input conditions, e.g., the firstorder statistics of a digitized light microscopic nuclear image may be influenced by variations in staining, illumination or variations in the photographic process
Cell nuclei from early ovarian cancer. This retrospective study was performed on tissue samples from patients treated for early ovarian cancer during 1982–1989. 134 cases of ovarian cancer classified as International Federation of Gynecology and Obstetrics (FIGO) stage I were included in the analysis. 94 cases had a good prognosis, which means that they survived the follow-up period without a relapse
Summary
Nuclear texture analysis is a useful method to obtain quantitative information for the diagnosis and prognosis of human cancer [6,9,12,14,15,26], and gives information about the spatial arrangement of the pixel gray levels in a digitized microscopic nuclear image. The extracted statistics may range from simple first-order statistics, such as mean value and standard deviation of the gray level distribution, to second-order or higher order statistics depending on the number of pixels which define the local information. 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. 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
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