Einsatz digitaler Bildanalyse zur Unterscheidung Vaskulärer Anomalien im Kopf-Hals-Bereich

The histological differentiation of different types of vascular anomalies, such as lymphangioma, hemangioma, paraganglioma, venous malformations, arteriovenous malformations, pyogenic granuloma, and (not otherwise specified) vascular malformations is very difficult due to the heterogeneity of these anomalies. The present study therefore investigated whether an immunohistological quantification of vascular anomaly tissues by means of digital image analysis, allows a more accurate assignment into their different subtypes. Vascular anomaly tissues of 40 patients were examined immunohistologically by staining the samples with a selection of five vascular endothelial-associated markers (CD31, CD34, CLDN5, PDPN, VIM). The staining was documented microscopically followed by digital image analyses and quantification of the candidate-marker-proteins within the tissues. The aim of the study was to evaluate digital image analysis as a potential method to distinguish vascular anomalies. Differences in the expression pattern of the candidate proteins could be detected by the ratios (quotients) of the digitally recorded and quantified immunohistochemical signal values. In the group of pyogenic granulomas, quotients of CLDN5/CD34 (p<0.01) and VIM/CD34 (p<0.05) were found to differentiate this tissue entity from other vascular anomalies (Figure 47 A-C). In contrast, PDPN/CLDN5 (p<0.001) and PDPN/CD34 (p<0.01) were useful for differentiation of lymphangiomas from all other tissues of vascular anomalies (Figure 47 A-D). As expected, the use of the well-established endothelial markers CD34 and CD31 exhibited major immune reactivity of vascular endothelia. The usefulness of PDPN to distinguish vascular anomalies of lymphatic origin from other malformations could be demonstrated. This underlines the reliability of PDPN as a lymphatic marker, that allows differentiation of vascular anomalies. The detection of CLDN5 in the endothelium of vascular anomalies supports its previously postulated role in differentiation and maintenance of vascular structures. With regards to future diagnostic methods, it can be expected that the use of digital image analysis will continue to increase. The aim should therefore be to establish digital methods for histological evaluation of tissue samples. Digital image analysis proves to be a promising tool for measuring differences in the expression of markers such as those used in vascular anomaly tissues. Results of this study support the usefulness of digital analysis for classification of the heterogeneous group of vascular anomalies. However, it should be emphasized, that its potential application cannot replace an experienced pathologist, abut rather assist in the diagnosis. In addition it is important to note, that the immunohistochemical results regularly have to be considered in the context of the overall clinical picture. There are still many inconsistencies among the histopathological and clinical classifications of anomalies in each subgroup. This can lead to misdiagnosis that negatively affects the choice of therapy. In this context, digital quantification can help optimizing diagnosis and make it more objective. Furthermore, it became obvious that diagnosis of vascular anomalies should not be restricted to one or a few immunohistochemical markers. Rather it is necessary to create an "expression profile" for each tissue, based on characteristic protein expression-patterns of the markers. In this study we were able to show that stratification of vascular anomaly tissues can already be achieved by deploying five marker proteins. Further studies which include a higher number of vascular anomaly tissues and additional marker proteins should follow, to complete the stratification of all vascular anomalies. This will be the goal of future investigations.