{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T05:20:59Z","timestamp":1765862459192,"version":"3.48.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to explore the significant factors of prognosis in patients with Moyamoya disease (MMD) after surgical revascularization and to develop a nomogram model for predicting poor prognosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed magnetic resonance imaging (MRI) and clinical data of 128 patients with MMD. The patients were randomly assigned to training and validation cohorts in a ratio of 7:3. Multivariate logistic regression analysis was applied to identify factors significantly associated with prognosis. The predictive efficiencies of the models were evaluated using receiver operating characteristic (ROC) curves and compared using the Delong test. We then developed a nomogram model for prediction and verified it using a validation cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Preoperative arterial spin labeling (ASL)-Alberta Stroke Program Early computed tomography Score (ASL-ASPECTS), admission modified Rankin scale (mRS) score, ivy sign, and Houkin\u2019s grade &amp;gt;2 were significantly associated with poor prognosis (mRS\u202f&amp;gt;\u202f2). The areas under the curves (AUCs) for predicting poor prognosis were 0.772, 0.855, 0.899, and 0.994 for clinical, conventional MRI, ASL-based, and combination models, respectively. The results of the Delong test demonstrated the superior prediction ability of the combination model compared with the clinical, conventional MRI, and ASL models (all\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001). Calibration curve analysis showed that the predictive probability of the nomogram model was highly consistent in the training cohort. The decision curve showed a net predictive benefit in the validation cohort.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Preoperative ASL-ASPECTS, admission mRS, ivy sign, and Houkin grade &amp;gt;2 were significantly associated with poor prognosis in patients with MMD after surgical revascularization. The nomogram model, including enrolled ASL-ASPECTS and MRI features, may help improve prognosis prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2025.1649224","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T05:17:27Z","timestamp":1765862247000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Arterial spin labeling-ASPECTS and a conventional MRI-based nomogram for predicting prognosis after surgical revascularization in Moyamoya disease"],"prefix":"10.3389","volume":"16","author":[{"given":"Tao","family":"Yuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenhua","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yawu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijuan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Lou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Geng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiying","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanmin","family":"Quan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"112034","DOI":"10.1016\/j.ejrad.2025.112034","article-title":"Multi-delay 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