Because histologically prominent microvascular proliferation is frequently present in glioblastoma multiforme, it has been hypothesized that this neoplasm is particularly dependent on neovascularization for its continued growth and that antiangiogenic therapy might be especially useful. To quantify the histological aspects of microvascular proliferation in glioma, a feasible and reproducible method was developed for computer-assisted image analysis of the visualized microvasculature in glial tissue. This method was used to compare several vascular parameters in histological whole-tumor sections of untreated human glioblastoma multiforme with those in histologically normal cerebral cortex and white matter. There was a significant increase in mean number, area, and perimeter of blood vessels per microscopic field in glioblastoma multiforme compared to normal cerebral white matter. In a substantial number of tumor fields, however, the vascular density was in the same range as that of normal cerebral white matter. The striking heterogeneity of the microvasculature within glioblastoma multiforme was illustrated by the significantly higher standard deviation for the vascular parameters in tumor tissue. The results of this study suggest that many regions of glioblastomas multiforme are not overtly angiogenesis dependent and may be difficult to treat by antiangiogenic therapy alone.

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Experimental diabetes leads to alterations in cellular components involved in the early phase of repair of intestinal anastomoses but not to a reduced accumulation of wound collagen.

Tumor budding is a promising and cost-effective biomarker with strong prognostic value in colorectal cancer. However, challenges related to interobserver variability persist. Such variability may be reduced by immunohistochemistry and computer-aided tumor bud selection. Development of computer algorithms for this purpose requires unequivocal examples of individual tumor buds. As such, we undertook a large-scale, international, and digital observer study on individual tumor bud assessment. From a pool of 46 colorectal cancer cases with tumor budding, 3000 tumor bud candidates were selected, largely based on digital image analysis algorithms. For each candidate bud, an image patch (size 256 × 256 µm) was extracted from a pan cytokeratin-stained whole-slide image. Members of an International Tumor Budding Consortium (n = 7) were asked to categorize each candidate as either (1) tumor bud, (2) poorly differentiated cluster, or (3) neither, based on current definitions. Agreement was assessed with Cohen's and Fleiss Kappa statistics. Fleiss Kappa showed moderate overall agreement between observers (0.42 and 0.51), while Cohen's Kappas ranged from 0.25 to 0.63. Complete agreement by all seven observers was present for only 34% of the 3000 tumor bud candidates, while 59% of the candidates were agreed on by at least five of the seven observers. Despite reports of moderate-to-substantial agreement with respect to tumor budding grade, agreement with respect to individual pan cytokeratin-stained tumor buds is moderate at most. A machine learning approach may prove especially useful for a more robust assessment of individual tumor buds.

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