ArticleAJNR. American journal of neuroradiology2025
Data-Driven Prognostication in Distal Medium Vessel Occlusions Using Explainable Machine Learning.
Article in AJNR. American journal of neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- MeVO: To Treat or Not To Treat? Review of the Latest Evidence.Current neurology and neuroscience reports · 2026Review
- Redefining Ischemic Core, Penumbra, and Target Mismatch on Perfusion Imaging in Acute Anterior Distal Medium Vessel Occlusion.Stroke (Hoboken, N.J.) · 2025Article
- The Cortical Vein Opacification Score Is Independently Associated with Good and Excellent Functional Outcomes at 90 Days in Patients with Minor Stroke with Anterior Circulation Large-Vessel Occlusion: A Multicenter Study.AJNR. American journal of neuroradiology · 2025Article
- Predicting Early Outcomes of Prostatic Artery Embolization UsingDiagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeDistal medium vessel occlusions (DMVOs) are estimated to cause acute ischemic stroke in 25%-40% of cases. Prognostic models can inform patient counseling and research by enabling outcome predictions. However, models designed specifically for DMVOs are lacking. MATERIALS AND
methodsThis retrospective study developed a machine learning model to predict 90-day unfavorable outcome (defined as an mRS score of 3-6) in 164 patients with primary DMVO. A model developed with the TabPFN algorithm used selected clinical, laboratory, imaging, and treatment data with the least absolute shrinkage and selection operator feature selection. Performance was evaluated via 5-repeat 5-fold cross-validation. Model discrimination and calibration were evaluated. SHapley Additive Explanations (SHAP) identified influential features. A Web application deployed the model for individualized predictions.
resultsThe model achieved an area under the receiver operating characteristic curve of 0.815 (95% CI, 0.79-0.841) for predicting unfavorable outcome, demonstrating good discrimination, and a Brier score of 0.19 (95% CI, 0.177-0.202), demonstrating good calibration. SHAP analysis ranked admission NIHSS score, premorbid mRS, type of thrombectomy, modified TICI score, and history of malignancy as top predictors. The Web application enables individualized prognostication.
conclusionsOur machine learning model demonstrated good discrimination and calibration for predicting 90-day unfavorable outcomes in primary DMVO strokes. This study demonstrates the potential for personalized prognostic counseling and research to support precision medicine in stroke care and recovery.
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