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Architectural correlates of genotype have not been systematically investigated. This study aimed to assess the reticulin framework as a potential morphologic structural surrogate of molecular background and to introduce a novel deep learning model for its spatially resolved quantitative analysis and genotype prediction. A total of 104 adrenal and extra-adrenal PGLs with complete clinical, pathological, and genetic data were retrospectively analyzed. Reticulin stain was evaluated qualitatively and quantitatively using a supervised convolutional neural network trained on expert-annotated reticulin-stained whole-slide images (WSIs) to map and quantify areas of intact framework and very small nest patterns. Two bias-reduced logistic regression models (Firth\u2019s method) were developed to predict germline cluster 1 genotype, each combining clinical variables (age, tumor size, extra-adrenal presentation) with one artificial intelligence (AI)-derived morphometric feature-percentage of intact framework (Model-INTACT) or very small nests (Model-VSN). PGLs harboring germline cluster 1 variants occurred at a younger age, were larger, more frequently extra-adrenal, and showed significant enrichment of intact reticulin and very small nest patterns compared with cases harboring germline cluster 2 variants and sporadic cases. The supervised AI model accurately mapped and quantified these architectural features across the WSIs. Predictive models integrating AI-derived morphometrics with clinical variables achieved excellent discrimination for germline cluster 1 genotype (AUC 0.981 for Model-INTACT; AUC 0.990 for Model-VSN). Preservation of the reticulin framework, particularly with very small nests, represents a histoarchitectural correlate of pseudohypoxic PGLs. Integration of AI-based morphometric descriptors with clinical parameters enables reliable pre-test prediction of germline cluster 1 genotype, bridging conventional histopathology and molecular classification.<\/jats:p>","DOI":"10.1007\/s12022-026-09904-4","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T07:44:02Z","timestamp":1771400642000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Tissue Architecture To Genetic Signature: Artificial intelligence-based Analysis of Reticulin Framework and Clinical Variables Predicts Molecular Cluster in Paragangliomas"],"prefix":"10.1007","volume":"37","author":[{"given":"Eleonora","family":"Duregon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mirko","family":"Parasiliti-Caprino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulia","family":"Orlando","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna Paola","family":"Ferrero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martina","family":"Bollati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rute","family":"Pedrosa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Darshan","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Giraudo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara","family":"Pasini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ezio","family":"Ghigo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuela","family":"Arvat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Volante","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauro","family":"Maccario","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauro","family":"Papotti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"9904_CR1","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/s12022-022-09704-6","volume":"33","author":"O Mete","year":"2022","unstructured":"Mete O, Asa SL, Gill AJ, et al (2022) Overview of the 2022 WHO Classification of Paragangliomas and Pheochromocytomas. 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