In this paper, wavelets were employed for multiscale image analysis to extract parameters for the description of chromatin texture in the cytological diagnosis and grading of invasive breast cancer. Their value was estimated by comparing the performance of co‐occurrence, densitometric, and morphometric parameters in an automated K‐nearest neighbor (Knn) classification scheme based on light microscopic images of isolated nuclei of paraffin‐embedded tissue. This design allowed a multifaceted cytological retrospective study of which the practical value can be judged easily. Results show that wavelets perform excellently with classification scores comparable with densitometric and co‐occurrence features. Moreover, because wavelets showed a high additive value with the other textural groups, this panel allowed a very profound description with higher recognition scores than previously reported (76% for individual nuclei, 100% for cases). Morphometric parameters performed less well and only slightly increased correct classification. The major drawback, besides image segmentation errors demanding operator supervision, emanated to be the few false‐negative cases, which restrict the immediate practical use. However, an enlargement of the parameter set may avoid this misclassification, resulting in an applicable expert system of practical use. Cytometry 33:32–40, 1998. © 1998 Wiley‐Liss, Inc.

Summary Tumour stromal neovascularization was investigated in 114 invasive and 20 in situ carcinomas of the uterine cervix by staining representative sections with the specific endothelial marker anti CD31 (clone JC/70A, isotope IgG1). A digital image analyser was used to measure the immunoreactivity. The following parameters were determined in the 'hot spots': vessel counts, vessel perimeter and endothelial stained area (expressed per mm2). The results were correlated with clinical and histopathological data. There was no significant relationship between the histopathological findings (tumour histology, tumour differentiation, FIGO stage, presence of lymph node metastasis or lymphovascular space involvement) and the median vessel count. In a univariate analysis all angiogenesis parameters had prognostic value: a higher vascularity was associated with worse prognosis (P < 0.05). Multiple regression analysis showed that vascular permeation (P < 0.001) and the median vessel count (P = 0.005) were the most important prognostic indicators. In the future these criteria may be used for selection of patients for anti-angiogenesis therapy.

Fibrous intimal thickening at implantation is a determinant risk factor for the functional and morphologic outcome of cadaveric renal allografts at 1 1/2 years.

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Background: Malignant mesothelioma, a mesoderm‐derived tumor, is related to asbestos exposure and remains a diagnostic challenge because none of the genetic or immunohistochemical markers have yet been proven to be specific. To assist in the identification of mesothelioma and to differentiate it from other common lesions at the same location, we have tested the performance of syntactic structure analysis (SSA) in an automated classification procedure. Materials and Methods: Light‐microscopic images of tissue sections of malignant mesothelioma, hyperplastic mesothelium, and adenocarcinoma were analyzed using parameters selected from the Voronoi diagram, Gabriel's graph, and the minimum spanning tree which were classified with a K‐nearest‐neighbor algorithm. Results: Results showed that mesotheliomas were diagnosed correctly in 74% of the cases; 76% of the adenocarcinomas were correctly graded, and 88% of the mesotheliomas were correctly typed. The performance of the parameters was dependent on the obtained classification (i.e., tumor‐tumor versus tumor‐benign). Conclusions: Our results suggest that SSA is valuable in the differential classification of mesothelioma and that it supplements a visually appraised diagnosis. The recognition scores may be increased by a combination of SSA with, for example, cellular or nuclear parameters, measured at higher magnifications to form a solid base for fully automated expert systems. Cytometry 35:23–29, 1999. © 1999 Wiley‐Liss, Inc.