Authors - Bharathi A, Mohan Kumar P, Subha B Abstract - Rupture of an intracranial aneurysm results in catastrophic subarachnoid hemorrhage with a 30–40% fatality rate. Although treatment decisions are guided by clinical risk scores (PHASES, ELAPSS), recent research suggests that morphological analysis and computational fluid dynamics (CFD) may offer better rupture prediction. This study looked at 92 middle cerebral artery aneurysms from the CMHA dataset, which included 71 that had ruptured and 21 that had not. We evaluated four feature sets: Clinical-Basic (13 variables), Clinical-Scores (adding PHASES and ELAPSS; 15 variables), Scores and Morphology (24 variables), and Full (28 variables). We trained logistic regression models using 5- fold cross-validation with a 20% test set. We used bootstrap validation (1000 iterations) and Bonferroni-corrected feature importance analysis to reduce overfitting. The AUC for the Clinical-Basic set was 0.891±0.063. Performance was enhanced to a maximum AUC of 0.976±0.034 by adding PHASES and ELAPSS. The Full model achieved an AUC of 0.981±0.029, with neither morphological nor hemodynamic variables giving much further improvement. Significant variance was revealed by bootstrap analysis (95% CI: 0.764-0.998). At 90% specificity, the test set's AUC was 0.933, but its sensitivity was only 14.3%. The primary contributors were ELAPSS (F=143.2, p<10⁻¹) and PHASES (F=38.4, p<10⁻¹), whereas morphological and hemodynamic characteristics did not exhibit any significant correlations. Clinical scores demonstrated strong discrimination, but CFD-derived parameters offered minimal additional value in this small, imbalanced, single-center group. The wide confidence intervals and class imbalance limit clinical recommendations. Further validation in larger, multicenter studies is necessary.
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