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Multicenter Study
. 2025 Dec 1;8(12):e2550772.
doi: 10.1001/jamanetworkopen.2025.50772.

Development and Validation of a Prediction Model for Intracranial Aneurysm Rupture Risk

Soichiro Fujimura  1   2 , Takeshi Yanagisawa  3 , Genki Kudo  3 , Toshiki Koshiba  4 , Masaaki Suzuki  5 , Hiroyuki Takao  2   3 , Toshihiro Ishibashi  3 , Hayato Ohwada  4 , Shigeo Yamashiro  6 , Maarten J Kamphuis  7   8 , Laura T van der Kamp  7 , Robert W Regenhardt  9 , Mervyn D I Vergouwen  7 , Gabriel J E Rinkel  7 , Aman B Patel  9 , Yuichi Murayama  3
Affiliations
Multicenter Study

Development and Validation of a Prediction Model for Intracranial Aneurysm Rupture Risk

Soichiro Fujimura et al. JAMA Netw Open. .

Abstract

Importance: Unruptured intracranial aneurysms (UIAs) affect 3.2% of the general population, and approximately 85% of subarachnoid hemorrhages result from their rupture. Despite their classification as low risk by prediction tools such as PHASES (population, hypertension, age, size of aneurysm, earlier subarachnoid hemorrhage from another aneurysm, and site of aneurysm) and the Unruptured Cerebral Aneurysm Study (UCAS), UIAs less than 10 mm are susceptible to rupture.

Objective: To develop and externally validate a machine-learning model (MLM) predicting rupture risk of UIAs.

Design, setting, and participants: This retrospective multicenter prognostic study analyzed UIAs from 4 institutions across 3 continents from January 2003 to November 2022. Each UIA was characterized by 29 clinical and 18 morphological variables. For model development, patients with UIAs were drawn from a large institutional cohort. Statistical analysis was performed from April 2024 to March 2025.

Exposure: An MLM based on the Light Gradient Boosting Machine algorithm was trained, and performance was assessed for validation externally.

Main outcomes and measures: The primary outcome was aneurysm rupture within 2 years after risk evaluation. Model performance was assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and the area under the receiver operating characteristic curve (AUROC) with 95% CIs.

Results: Drawing from 11 579 UIAs from multiple institutions of 8846 patients, there were 2750 patients with 3312 UIAs in the development cohort (median [IQR] age, 65.3 [54.9-73.6] years; 1856 females [67.5%]) and 1153 patients with 1501 UIAs in the external cohort (median [IQR] age, 63.6 [53.9-70.9] years; 828 females [71.8%]), for whom the MLM demonstrated a robust performance in risk estimation. In the development cohort, 71 UIAs (2.1%) ruptured during 8.5 years' median follow-up (IQR, 5.1-11.6 years), and in the external cohort, 48 UIAs (3.2%) ruptured during a median (IQR) follow-up of 5.4 (3.7-8.7) years. With a threshold of 0.52 for the MLM, performance in the development cohort for sensitivity was 0.78 (95% CI, 0.67-0.86); specificity, 0.82 (95% CI, 0.80-0.83); PPV, 0.09 (95% CI, 0.07-0.11); NPV, 0.99 (95% CI, 0.99-1.00); PLR, 4.23 (95% CI, 3.66-4.89); NLR, 0.28 (95% CI, 0.18-0.42); and AUROC, 0.88 (95% CI, 0.85-0.92) and in the external cohort, sensitivity was 0.90 (95% CI, 0.78-0.95); specificity, 0.70 (95% CI, 0.67-0.72); PPV, 0.09 (95% CI, 0.07-0.12); NPV, 1.00 (95% CI, 0.99-1.00); PLR, 2.94 (95% CI, 2.60-3.33); NLR, 0.15 (95% CI, 0.07-0.34); and AUROC, 0.90 (95% CI, 0.85-0.94). The MLM performed consistently in UIAs less than 10 mm (AUROC, 0.88 [95% CI, 0.82-0.94]), suggesting potential complementary value to PHASES and the UCAS.

Conclusions and relevance: In this prognostic study, the MLM consistently identified features of UIAs as significantly associated with rupture across different cohorts. These results support the MLM's potential to assist physicians and patients in UIA treatment decisions.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Fujimura reported receiving a technology development research grant to the institution from the Nakatani Foundation, a research encouragement grant to the institution from The Uehara Memorial Foundation, and other funding as a shareholder from NiDUS Inc outside the submitted work. Mr Kudo reported receiving other funding as a shareholder from DataForm Co, Ltd outside the submitted work. Mr Koshiba reported receiving personal fees from MediEng Co, Ltd outside the submitted work. Dr Takao reported receiving grants to the university from The Nippon Foundation and personal fees to the individual from the Digital Agency outside the submitted work. Dr Ishibashi reported receiving personal fees from Medtronic Japan outside the submitted work. Dr Yamashiro reported receiving nonfinancial support from AstraZeneca outside the submitted work. Dr Regenhardt reported receiving research grants from the National Institutes of Health, the Society of Vascular and Interventional Neurology, and the Heitman Foundation and other funding as a site principal investigator from Penumbra, MicroVention, Stryker, Kaneka, and Vesalio and as a data and safety monitoring board member from Rapid Medical outside the submitted work. Dr Patel reported receiving personal fees from Stryker, MicroVention, Penumbra, Medtronic, and Kaneka outside the submitted work. Dr Murayama reported receiving grants to the institution from Stryker and Siemens; personal fees to the individual from Stryker, Century Medical, Tokai Medical Products, Kaneka Medics, and Allm Inc; and other funding as a shareholder from Spine-Tech Inc and NiDUS Inc and having a patent as an inventor with royalties paid to the individual by Allm Inc outside the submitted work. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Flowchart Describing the Patient Selection
Unruptured intracranial aneurysms registered during outpatient visits between January 2003 and November 2022 were included. The machine-learning models were developed and evaluated using aneurysms that met the inclusion criteria. EVT indicates endovascular treatment.
Figure 2.
Figure 2.. Development and External Validation of the Machine-Learning Model (MLM) and Receiver Operating Characteristic (ROC) Curves
The rupture risk indicator is shown for the development (A) and external validation (B) cohorts. Each plot point represents an individual intracranial aneurysm. ROC curves are displayed for the MLM, PHASES (population, hypertension, age, size of aneurysm, earlier subarachnoid hemorrhage from another aneurysm, and site of aneurysm), and Unruptured Cerebral Aneurysm Study (UCAS), along with the corresponding area under the ROC (AUROC), values and 95% CIs. For PHASES, scores range from 0 to 2, with higher scores indicating greater predicted rupture risk; for the UCAS, scores range from 0 to 15, with higher scores indicating greater predicted rupture risk.
See this image and copyright information in PMC

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