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It aims to enhance the diagnosis of often misdiagnosed skin diseases such as eczema, lymphoma, and melanoma. The novel contribution lies in integrating Radiomic features with GNNs and comparing their efficacy against traditional multi-stain profiles.\n<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>\n              <jats:bold>Methods<\/jats:bold>\n            <\/jats:title>\n            <jats:p>We utilized GNNs to process multiple pathological slides as cell-level graphs, comparing their performance with XGBoost and Random Forest classifiers. The analysis included two feature types: multi-stain profiles and Radiomic features. Dimensionality reduction techniques such as UMAP and t-SNE were applied to optimize the feature space, and graph connectivity was based on spatial and feature closeness.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>\n              <jats:bold>Results<\/jats:bold>\n            <\/jats:title>\n            <jats:p>Integrating Radiomic features into spatially connected graphs significantly improved classification accuracy over traditional models. The application of UMAP further enhanced the performance of GNNs, particularly in classifying diseases with similar pathological features. The GNN model outperformed baseline methods, demonstrating its robustness in handling complex histopathological data.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>\n              <jats:bold>Conclusion<\/jats:bold>\n            <\/jats:title>\n            <jats:p>Radiomic features processed through GNNs show significant promise for multi-disease classification, improving diagnostic accuracy. This study\u2019s findings suggest that integrating advanced imaging analysis with graph-based modeling can lead to better diagnostic tools. Future research should expand these methods to a wider range of diseases to validate their generalizability and effectiveness.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s11548-024-03277-x","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T13:03:02Z","timestamp":1728306182000},"page":"497-505","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8232-8920","authenticated-orcid":false,"given":"Luis Carlos","family":"Rivera Monroy","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9589-3774","authenticated-orcid":false,"given":"Leonhard","family":"Rist","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Ostalecki","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Bauer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3076-5122","authenticated-orcid":false,"given":"Julio","family":"Vera","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7600-5869","authenticated-orcid":false,"given":"Katharina","family":"Breininger","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9550-5284","authenticated-orcid":false,"given":"Andreas","family":"Maier","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"issue":"1","key":"3277_CR1","first-page":"43","volume":"100","author":"A Chopra","year":"2020","unstructured":"Chopra A, Sharma R, Rao UN (2020) Pathology of melanoma. 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The samples obtained for MELC technology are archived in an officially established (by the Institute of Medical Documentation in Erlangen, Germany) tissue bank. A patient information sheet or informed consent form was developed in agreement with the ethics committee to collect the samples. After consultation with the ethics committee, MELC analysis is equivalent to extended immunofluorescence diagnostics as routinely used for histological diagnostics. A corresponding ethics application has been approved for this project (Application no.: 43_18 B).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}