{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T04:50:06Z","timestamp":1788497406574,"version":"build-2803163510"},"reference-count":44,"publisher":"American Medical Association (AMA)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To develop and externally validate a machine-learning model (MLM) predicting rupture risk of UIAs.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Exposure<\/jats:title>\n An MLM based on the Light Gradient Boosting Machine algorithm was trained, and performance was assessed for validation externally.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Drawing from 11\u202f579 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\u2019 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.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n In this prognostic study, the MLM consistently identified features of UIAs as significantly associated with rupture across different cohorts. These results support the MLM\u2019s potential to assist physicians and patients in UIA treatment decisions.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2025.50772","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T16:00:27Z","timestamp":1766505627000},"page":"e2550772","source":"Crossref","is-referenced-by-count":12,"title":["Development and Validation of a Prediction Model for Intracranial Aneurysm Rupture Risk"],"prefix":"10.1001","volume":"8","author":[{"given":"Soichiro","family":"Fujimura","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Tokyo University of Science, Katsushika-ku, Tokyo, Japan"},{"name":"Division of Innovation for Medical Information Technology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeshi","family":"Yanagisawa","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Genki","family":"Kudo","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshiki","family":"Koshiba","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, Tokyo University of Science, Noda-shi, Chiba, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masaaki","family":"Suzuki","sequence":"additional","affiliation":[{"name":"Faculty of Liberal Arts and Sciences, Chukyo University, Showa-ku, Nagoya-shi, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroyuki","family":"Takao","sequence":"additional","affiliation":[{"name":"Division of Innovation for Medical Information Technology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan"},{"name":"Department of Neurosurgery, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshihiro","family":"Ishibashi","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hayato","family":"Ohwada","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, Tokyo University of Science, Noda-shi, Chiba, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shigeo","family":"Yamashiro","sequence":"additional","affiliation":[{"name":"Division of Neurosurgery, Department of Cerebrovascular Medicine and Surgery, Saiseikai Kumamoto Hospital, Minami-ku, Kumamoto, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maarten J.","family":"Kamphuis","sequence":"additional","affiliation":[{"name":"Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands"},{"name":"Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura T.","family":"van der Kamp","sequence":"additional","affiliation":[{"name":"Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert W.","family":"Regenhardt","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mervyn D. 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