Abstract
BackgroundThe histological differentiation of individual types of vascular anomalies (VA), such as lymphatic malformations (LM), hemangioma (Hem), paraganglioma (PG), venous malformations (VeM), arteriovenous malformations (AVM), pyogenic granulomas (GP), and (not otherwise classified) vascular malformations (VM n.o.c.) is frequently difficult due to the heterogeneity of these anomalies. The aim of the study was to evaluate digital image analysis as a method for VA stratificationMethodsA total of 40 VA tissues were examined immunohistologically using a selection of five vascular endothelial-associated markers (CD31, CD34, CLDN5, PDPN, VIM). The staining results were documented microscopically followed by digital image analyses based quantification of the candidate-marker-proteins using the open source program ImageJ/Fiji.ResultsDifferences in the expression patterns of the candidate proteins could be detected particularly when deploying the quotient of the quantified immunohistochemical signal values. Deploying signal marker quotients, LM could be fully distinguished from all other tested tissue types. GP achieved stratification from LM, Hem, VM, PG and AVM tissues, whereas Hem, PG, VM and AVM exhibited significantly different signal marker quotients compared with LM and GP tissues.ConclusionAlthough stratification of different VA from each other was only achieved in part with the markers used, the results of this study strongly support the usefulness of digital image analysis for the stratification of VA. Against the background of upcoming new diagnostic techniques involving artificial intelligence and deep (machine) learning, our data serve as a paradigm of how digital evaluation methods can be deployed to support diagnostic decision making in the field of VAs.
Highlights
Vascular anomalies (VA) of the head and neck area are a heterogeneous group of vascular diseases which can cause cosmetic and life-threatening functional disorders, such as bleeding, dyspnea or dysphagia [1,2,3]
Mutations in the PIK3CA gene were found associated with the development of venous and lymphatic malformations or syndromes including these types of vascular anomalies (VA) [18]
Quantitative digital image analysis was evaluated as an instrument for VA stratification to support the histopathological diagnosis
Summary
Vascular anomalies (VA) of the head and neck area are a heterogeneous group of vascular diseases which can cause cosmetic and life-threatening functional disorders, such as bleeding, dyspnea or dysphagia [1,2,3]. VA encompass vascular malformations such as venous (VeM), arteriovenous (AVM) and lymphatic malformations (LM) and paraganglioma (PG) Another subgroup are vascular tumors such as hemangioma (Hem) and pyogenic granuloma (GP). Diagnosis of VA subtypes is typically performed by histopathological evaluation in conjunction with the clinical appearance of the VA [4]. In this context, digital quantification may help optimizing diagnosis thereby making it more objective. The histological differentiation of individual types of vascular anomalies (VA), such as lymphatic malformations (LM), hemangioma (Hem), paraganglioma (PG), venous malformations (VeM), arteriovenous malformations (AVM), pyogenic granulomas (GP), and (not otherwise classified) vascular malformations (VM n.o.c.) is frequently difficult due to the heterogeneity of these anomalies. Against the background of upcoming new diagnostic techniques involving artificial intelligence and deep (machine) learning, our data serve as a paradigm of how digital evaluation methods can be deployed to support diagnostic decision making in the field of VAs
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