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Peer-reviewedCC BY 4.0Frontiers in Neuroscience

Multiparametric MRI radiomics and machine learning for outcome prediction in acute ischemic stroke: a retrospective study of 353 patients

  • Xiaoxing Peng1
  • Jiannan Guo2
  • Qiuyan Zhou3
  • Shunyu Wang4
  • Tingting Li5
5 affiliations
  1. 1Department of Medical Imaging, Affiliated Hospital of Changchun University of Chinese Medicine
  2. 2Department of Electro-diagnosis, Affiliated Hospital of Changchun University of Chinese Medicine
  3. 3Department of Lung Disease, Tumor and Blood Center, Lung Oncology, Affiliated Hospital of Changchun University of Chinese Medicine
  4. 4Department of Catheterization Room, Affiliated Hospital of Changchun University of Chinese Medicine
  5. 5Department of Electro-diagnosis, Qian Wei Hospital of Jilin Province
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Abstract

Background: Acute ischemic stroke (AIS) remains a leading cause of death and disability worldwide. Early prediction for clinical outcomes is essential for guiding therapy. Multiparametric Magnetic Resonance Imaging (MRI) offers detailed characterization of ischemic tissue and may predict prognosis in AIS patients. This study assesses the prognostic value of multiparametric MRI parameters in predicting functional outcome, recurrence risk and mortality in AIS patients.

Methods: We conducted a retrospective analysis of 353 AIS patients who underwent multiparametric MRI within 24 h of onset. MRI metrics including diffusion-weighted imaging (DWI) lesion volume, ADC minimum, perfusion parameters and Susceptibility-Weighted Imaging (SWI) microbleeds were correlated with National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS) scores at discharge and 90 days. Predictive performance for clinical outcomes was assessed by receiver-operating characteristic (ROC) analysis.

Results: Among 353 AIS patients with a mean door-to-imaging time of 27 ± 10 min, higher NIHSS, larger DWI volume, prolonged mean transit time (MTT) and Tmax, and increased microbleeds significantly correlated with mortality and disability, while lower ADC, CBF, and CBV correlated inversely with clinical outcomes. NIHSS ≥10.5 and DWI lesion volume ≥52.5 mL yielded AUCs of 0.963 and 0.975 and accuracies of 91.35% and 92.79%, respectively; CBF ≤ 20.5 yielded an AUC of 0.975. NIHSS ≥8.5 and MTT ≥8.5 s showed AUCs of 0.948 and 0.931, respectively, for predicting hemorrhagic transformation. Perfusion thresholds modestly predicted 90-day recurrence (AUC = 0.7). Hypertension, diabetes, and prior stroke were significant clinical predictors of poor outcome. These predictive metrics reflect internal, within-cohort validation only; independent external validation is required before clinical application.

Conclusion: Multiparametric MRI provides robust prognostic information in AIS. Quantitative thresholds for perfusion and diffusion parameters help in risk stratification for hemorrhage and mortality, enhancing individualized management and outcome prediction.

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Cite

BibTeXRIS
BibTeX
@article{peng2026multiparametric,
  title = {{Multiparametric MRI radiomics and machine learning for outcome prediction in acute ischemic stroke: a retrospective study of 353 patients}},
  author = {Xiaoxing Peng and Jiannan Guo and Qiuyan Zhou and Shunyu Wang and Tingting Li},
  journal = {Frontiers in Neuroscience},
  year = {2026},
  month = oct,
  doi = {10.3389/fnins.2026.1843917},
  url = {https://www.frontiersin.org/articles/10.3389/fnins.2026.1843917/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Multiparametric MRI radiomics and machine learning for outcome prediction in acute ischemic stroke: a retrospective study of 353 patients
AU  - Xiaoxing Peng
AU  - Jiannan Guo
AU  - Qiuyan Zhou
AU  - Shunyu Wang
AU  - Tingting Li
PY  - 2026
DA  - 2026/10/22
JO  - Frontiers in Neuroscience
DO  - 10.3389/fnins.2026.1843917
UR  - https://www.frontiersin.org/articles/10.3389/fnins.2026.1843917/full
AB  - Background: Acute ischemic stroke (AIS) remains a leading cause of death and disability worldwide. Early prediction for clinical outcomes is essential for guiding therapy. Multiparametric Magnetic Resonance Imaging (MRI) offers detailed characterization of ischemic tissue and may predict prognosis in AIS patients. This study assesses the prognostic value of multiparametric MRI parameters in predicting functional outcome, recurrence risk and mortality in AIS patients.  Methods: We conducted a retrospective analysis of 353 AIS patients who underwent multiparametric MRI within 24 h of onset. MRI metrics including diffusion-weighted imaging (DWI) lesion volume, ADC minimum, perfusion parameters and Susceptibility-Weighted Imaging (SWI) microbleeds were correlated with National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS) scores at discharge and 90 days. Predictive performance for clinical outcomes was assessed by receiver-operating characteristic (ROC) analysis.  Results: Among 353 AIS patients with a mean door-to-imaging time of 27 ± 10 min, higher NIHSS, larger DWI volume, prolonged mean transit time (MTT) and Tmax, and increased microbleeds significantly correlated with mortality and disability, while lower ADC, CBF, and CBV correlated inversely with clinical outcomes. NIHSS ≥10.5 and DWI lesion volume ≥52.5 mL yielded AUCs of 0.963 and 0.975 and accuracies of 91.35% and 92.79%, respectively; CBF ≤ 20.5 yielded an AUC of 0.975. NIHSS ≥8.5 and MTT ≥8.5 s showed AUCs of 0.948 and 0.931, respectively, for predicting hemorrhagic transformation. Perfusion thresholds modestly predicted 90-day recurrence (AUC = 0.7). Hypertension, diabetes, and prior stroke were significant clinical predictors of poor outcome. These predictive metrics reflect internal, within-cohort validation only; independent external validation is required before clinical application.  Conclusion: Multiparametric MRI provides robust prognostic information in AIS. Quantitative thresholds for perfusion and diffusion parameters help in risk stratification for hemorrhage and mortality, enhancing individualized management and outcome prediction.
SN  - 1662-453X
SN  - 1662-4548
ER  -