{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T16:00:10Z","timestamp":1791388810358,"version":"4.3.3"},"reference-count":58,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T00:00:00Z","timestamp":1771113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Traditional statistical approaches identify group-level associations between biomarkers and rupture status in intracranial aneurysms (IAs) but often miss nonlinear interactions at the patient level. Methods: The authors retrospectively analyzed 35 saccular IAs in 35 patients (57.1% ruptured) from a single center (2021\u20132023). Demographics, detailed morphology (e.g., neck width, aspect ratio, VERTI, irregular shape), and multi-site inflammatory\/immune markers (CRP; complement C3\/C4; IgA\/IgG\/IgM) were included. After preprocessing (min\u2013max scaling; one-hot encoding), five algorithms (DT, AdaBoost, GBM, XGBoost, RF) were evaluated with stratified five-fold CV and class balancing via random oversampling. The primary model (Random Forest) was tuned with Optuna and explained using global feature importance and LIME. The results showed that baseline RF achieved CV ROC-AUC 0.81 and test ROC-AUC 0.92 (test accuracy 0.857). The tuned RF (with oversampling and Optuna) yielded a mean CV accuracy of 0.85 \u00b1 0.09 and CV ROC-AUC of 0.98 \u00b1 0.07 while maintaining test ROC-AUC of 0.92. The average precision on the test PR curve was 0.97. The most influential predictors combined inflammatory markers (CRP, C3, C4) with morphology (neck width, irregular shape). LIME revealed consistent local patterns: low A.CRP\/C.CRP and lower C3\/C4 favored Not-Broken, whereas higher CRP\/complement with smaller neck and irregular shape pushed toward Broken classifications. It can be concluded that an interpretable machine learning (ML) pipeline captured clinically plausible, nonlinear interactions between inflammation and aneurysm geometry. Integrating explainable ML with conventional statistics may enhance rupture risk stratification, enable patient-level rationale, and inform personalized management. These results could significantly contribute to the quality of treatment for patients with intracranial aneurysms.<\/jats:p>","DOI":"10.3390\/bioengineering13020226","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T11:11:28Z","timestamp":1771240288000},"page":"226","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Interpretable Machine Learning Identifies Key Inflammatory and Morphological Drivers of Intracranial Aneurysm Rupture Risk"],"prefix":"10.3390","volume":"13","author":[{"given":"Epameinondas","family":"Ntzanis","sequence":"first","affiliation":[{"name":"Department of Radiology, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5416-1991","authenticated-orcid":false,"given":"Nikolaos","family":"Papandrianos","sequence":"additional","affiliation":[{"name":"Department of Energy Systems, School of Technology, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petros","family":"Zampakis","sequence":"additional","affiliation":[{"name":"Department of Interventional Neuroradiology\/Endovascular Neurosurgery, University Hospital of Patras, 26500 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasilios","family":"Panagiotopoulos","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University Hospital of Patras, 26500 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2664-2127","authenticated-orcid":false,"given":"Constantinos","family":"Koutsojannis","sequence":"additional","affiliation":[{"name":"Laboratory of Health Physics & Computational Intelligence, Department of Physiotherapy, School of Rehabilitations Sciences, University of Patras, 26500 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Kalogeropoulou","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2498-9661","authenticated-orcid":false,"given":"Elpiniki I.","family":"Papageorgiou","sequence":"additional","affiliation":[{"name":"Department of Energy Systems, School of Technology, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6440","DOI":"10.1038\/s41598-018-24732-1","article-title":"Morphological parameters and anatomical locations associated with rupture status of small intracranial aneurysms","volume":"8","author":"Duan","year":"2018","journal-title":"Sci. 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