Evaluation of digital image analysis as a supportive tool for the stratification of head and neck vascular anomalies
- PDF / 11,327,640 Bytes
- 14 Pages / 595.276 x 790.866 pts Page_size
- 13 Downloads / 173 Views
MISCELLANEOUS
Evaluation of digital image analysis as a supportive tool for the stratification of head and neck vascular anomalies Jovine Ehrenreich1 · Michael Bette2 · Ansgar Schmidt3 · Marion Roeßler3 · Udo Bakowsky4 · Urban W. Geisthoff1 · Boris A. Stuck1 · Robert Mandic1 Received: 12 March 2020 / Accepted: 27 May 2020 © The Author(s) 2020
Abstract Background 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. The aim of the study was to evaluate digital image analysis as a method for VA stratification Methods A total of 40 VA tissues were examined immunohistologically using a selection of five vascular endothelialassociated 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. Results Differences 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. Conclusion Although 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. Keywords Vascular anomaly · Head and neck · Stratification · Digital image analysis · Immunohistochemistry
Introduction
* Robert Mandic [email protected]‑marburg.de 1
Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Marburg, PhilippsUniversität Marburg, 3. BA, +3/08070 Baldingerstrasse, 35033 Marburg, Germany
2
Institute of Anatomy and Cell Biology, Philipps-Universität Marburg, Marburg, Germany
3
Institute of Pathology, University Hospital Giessen and Marburg, Marburg, Germany
4
Department of Pharmaceutical Technology and Biopharmacy, Philipps-Universität Marburg, Marburg, Germany
Vascular anomalies (VA) of the head and neck area are a heterogeneous group of vascular diseases which can not only cause cosmetic but also life-threatening functional disorders, such as bleeding, dyspnea or dysphagia [1–3]. VA encompass vascular malformations such as venou
Data Loading...
