Metastasizing APUD cell tumours of the human gastrointestinal tract. Light microscopic and karyometric studies

Three-dimensional confocal laser scanning DNA ploidy cytometry in thick histological sections

DNA ploidy measurement by flow (FCM) or image cytometry (ICM) of single cell suspensions of solid tumour has prognostic value, but it would be a definite advantage if the assessment could be done on histological sections. However, this is usually not possible by means of standard ICM, due to the capping of nuclei in thin sections, or overlap in thick sections. Three-dimensional (3D) microscopy by means of confocal laser scanning microscopy (CLSM) could solve this problem in theory but the results published so far are not very satisfactory. A new method has been developed in which the DNA content of haploid (human testis spermatozoa), diploid, tetraploid, octaploid (human and rat liver and human spermatogonia), and near-triploid (human breast cancer) nuclei stained with YOYO-1 iodide has been measured by a newly developed 3D image cytometry method (3DICM) in 20 microns thick histological sections. YOYO-1 iodide is a new highly sensitive, specific, stoichiometric, and stable fluorescent dye for DNA. DNA ploidy of a breast cancer which was near-triploid with FCM and ICM was also assessed with 3DICM in a tissue section adjacent to the section used for FCM and ICM and the results were compared. The integrated 3DICM fluorescence intensity showed good linearity (r = 0.99) with the real DNA content of all nuclei analysed. In human tissue, the coefficient of variation of 3DICM for haploid (n = 12), diploid (n = 63), triploid (n = 13), tetraploid (n = 12), and octaploid (n = 3) ploidy distributions was 5.1, 6.6, 4.2, 4.0, and 0.6 per cent, respectively (n = the number of nuclei). For the rat liver, the CV of the diploid (n = 21), tetraploid (n = 31), and octaploid (n = 3) peaks was 6.7, 4.8, and 1.6 per cent, respectively. Repeated "blind' measurements of nuclei with different DNA indices showed excellent reproducibility between different observers (r = 0.98). It is concluded that the 3DICM method used is accurate, reproducible, and clinically feasible in thick histological sections. This is especially important in small lesions, or if the results of DNA ploidy measurement of single cell suspensions (by FCM) or imprints (by ICM) are inadequate.

DNA image cytometry on sections as compared with image cytometry on smears and flow cytometry in melanoma

DNA measurements of 130 melanomas were carried out by flow cytometry (FCM) and image cytometry (ICM). ICM was applied to cytological preparations of fresh material (cICM) and to sections of formalin-fixed paraffin embedded tissue (sICM). The DNA ploidy, the DNA index of G0/G1 peaks (DI), and the proliferation index (PI) were used to compare all the methods. The following parameters reflecting malignancy were calculated only from ICM histograms: the 5c exceeding rate (5cER) and the malignancy grade (MG). In cases found to be DNA aneuploid by FCM, the PI values (FCM versus cICM) and the DIs (between all methods) showed a high correlation, and the concordance in relation to the DNA ploidy status was 96% (FCM versus cICM) and 94% (FCM versus sICM). However, we ascertained essential differences between FCM and ICM in melanomas classified as DNA diploid by FCM. The concordance in DNA ploidy was only 66% (FCM versus cICM) and 64% (FCM versus sICM). In contrast, cICM and sICM yielded similar results in most cases. With the exception of the near diploid range, ICM is superior to FCM in detecting DNA aneuploidy. In particular, DNA tetraploid stem lines can easily be overlooked by FCM. Therefore, DNA measurements of tumours judged to be DNA diploid by FCM must be verified by ICM. ICM on sections proved to be applicable and yielded reliable results provided that a suitable thickness was used, and the measuring of sectioned and overlapping nuclei was largely avoided by careful focusing in either direction.

Methods in quantitative image analysis

The main steps of image analysis are image capturing, image storage (compression), correcting imaging defects (e.g. non-uniform illumination, electronic-noise, glare effect), image enhancement, segmentation of objects in the image and image measurements. Digitisation is made by a camera. The most modern types include a frame-grabber, converting the analog-to-digital signal into digital (numerical) information. The numerical information consists of the grey values describing the brightness of every point within the image, named a pixel. The information is stored in bits. Eight bits are summarised in one byte. Therefore, grey values can have a value between 0 and 256 (2(8)). The human eye seems to be quite content with a display of 5-bit images (corresponding to 64 different grey values). In a digitised image, the pixel grey values can vary within regions that are uniform in the original scene: the image is noisy. The noise is mainly manifested in the background of the image. For an optimal discrimination between different objects or features in an image, uniformity of illumination in the whole image is required. These defects can be minimised by shading correction [subtraction of a background (white) image from the original image, pixel per pixel, or division of the original image by the background image]. The brightness of an image represented by its grey values can be analysed for every single pixel or for a group of pixels. The most frequently used pixel-based image descriptors are optical density, integrated optical density, the histogram of the grey values, mean grey value and entropy. The distribution of the grey values existing within an image is one of the most important characteristics of the image. However, the histogram gives no information about the texture of the image. The simplest way to improve the contrast of an image is to expand the brightness scale by spreading the histogram out to the full available range. Rules for transforming the grey value histogram of an existing image (input image) into a new grey value histogram (output image) are most quickly handled by a look-up table (LUT). The histogram of an image can be influenced by gain, offset and gamma of the camera. Gain defines the voltage range, offset defines the reference voltage and gamma the slope of the regression line between the light intensity and the voltage of the camera. A very important descriptor of neighbourhood relations in an image is the co-occurrence matrix. The distance between the pixels (original pixel and its neighbouring pixel) can influence the various parameters calculated from the co-occurrence matrix. The main goals of image enhancement are elimination of surface roughness in an image (smoothing), correction of defects (e.g. noise), extraction of edges, identification of points, strengthening texture elements and improving contrast. In enhancement, two types of operations can be distinguished: pixel-based (point operations) and neighbourhood-based (matrix operations). The most important pixel-based operations are linear stretching of grey values, application of pre-stored LUTs and histogram equalisation. The neighbourhood-based operations work with so-called filters. These are organising elements with an original or initial point in their centre. Filters can be used to accentuate or to suppress specific structures within the image. Filters can work either in the spatial or in the frequency domain. The method used for analysing alterations of grey value intensities in the frequency domain is the Hartley transform. Filter operations in the spatial domain can be based on averaging or ranking the grey values occurring in the organising element. The most important filters, which are usually applied, are the Gaussian filter and the Laplace filter (both averaging filters), and the median filter, the top hat filter and the range operator (all ranking filters). Segmentation of objects is traditionally based on threshold grey values. (AB