Conditional entropy as an indicator of pleomorphism in astrocytic tumors

Abstract

The entropies of nuclear arrangements as an indicator of pleomorphism are assessed using a morphometric method. Sixty astrocytic tumors (grades I, II, III and IV; 15 cases each) were reviewed and analyzed. All slides were stained with HE and MIB-1 antibody. The MIB-1 labeling index (LI) was assessed by counting nuclei under a microscope. Images of HE-stained slides were digitized and segmented using the watershed algorithm. Then, six nuclear parameters were measured automatically: (i) the number of total nuclei in the image, (ii) percentage of total nuclear area in the image, (iii) the mean area of the nucleus, (iv) the standard deviation of the area of the nucleus, (v) the entropy of nuclear arrangement (Entropysimple), and (vi) conditional entropy of nuclear arrangement (Entropyconditional). Entropysimple was defined according to the area of the nucleus and Entropyconditional was defined according to both the area of the nucleus and the area of its neighboring nuclei. Image processing and image analysis were performed with public domain software developed in the laboratory. Segmentation of the images resulted in inappropriate segmentation in a few percent of the images. The measurements obtained for each parameter were classified using discriminant analysis. The percentage of correct classification with Entropyconditional was 62%, which was the highest value among all the measurements. Classification based on the combination of all measurements resulted in a rate of correct classification of 88%. Thus, conditional entropy of nuclear arrangement is useful for grading of astrocytic tumors and it is proposed as an indicator of pleomorphism.

Low grade diffuse gliomas: Shared cellular composition and morphometric differences

“…Area total appeared to be more suitable than Number total 21 as an indicator of cellularity. Significant differences were observed in cellularity between some glioma types (Fig.…”

“…Tanaka et al. 21 developed the software “Gunmetry” for morphometric analysis of tumor tissue and showed that Entropy conditional , a parameter of structural pleomorphism, was a useful univariate indicator for grading astrocytic tumors. We utilized Entropy conditional in the present study and showed that O3 was clearly differentiated from DA and OA, confirming previous reports that O3 shows a more uniform histology than DA and OA.…”

“…Nuclear segmentation and morphometric measurements were performed using the public domain software Gunmetry 21 (http://sos.sourceforge.jp/index.php/Gunmetry) and coded in JAVA (Sun Microsystems, Santa Clara, CA, USA) as a plug‐in for the public domain image analytical software ImageJ (http://rsb.info.nih.gov/ij/). The marker watershed algorithm was used to extract regions from the image.…”

“…Six nuclear parameters in each segmented image were measured automatically: (i) the total number of nuclei in the image (Number total ); (ii) the percentage of total nuclear area in the image (Area total ); (iii) the mean area of the nucleus; (iv) the standard deviation of the area of the nucleus; (v) the entropy of the pleomorphism; and (vi) the conditional entropy of the pleomorphism (Entropy conditional ). Entropy conditional is defined as the randomness of nuclear areas between one nucleus and its neighbor 21 . Tanaka et al.…”

Proliferation and cell death of human glioblastoma cells after carbon‐ion beam exposure: Morphologic and morphometric analyses

“…The area of the digitized image was set at 640 × 480 pixels (1 pixel = 0.456 μm 2 of a slide) and saved in full‐color (24 bits RGB) BMP format. Nuclear segmentation (identification) and morphometric measurements were performed using public domain software (Gunmetry, http://sos.sourceforge.jp/), coded in JAVA (Sun Microsystems, Santa Clara, CA, USA) 20,21 …”