- 1Xi’an Central Hospital, Xi’an, China
- 2China-Japan Union Hospital of Jilin University, Changchun, China
- 3Tongchuan Mining Bureau Central Hospital, Tongchuan, China
Background: Acute Ischemic Stroke (AIS) remains a
leading cause of mortality and disability worldwide. Rapid and precise
prognostication of AIS is crucial for optimizing treatment strategies
and improving patient outcomes. This study explores the integration of
machine learning-derived radiomics signatures from multi-parametric MRI
with clinical factors to forecast AIS prognosis.(So predicting failure to recover? How does that help survivors?)
Objective: To develop and validate a nomogram
that combines a multi-MRI radiomics signature with clinical factors for
predicting the prognosis of AIS.
Methods: This retrospective study involved 506 AIS patients from two centers, divided into training (n = 277) and validation (n = 229)
cohorts. 4,682 radiomic features were extracted from T1-weighted,
T2-weighted, and diffusion-weighted imaging. Logistic regression
analysis identified significant clinical risk factors, which, alongside
radiomics features, were used to construct a predictive
clinical-radiomics nomogram. The model’s predictive accuracy was
evaluated using calibration and ROC curves, focusing on distinguishing
between favorable (mRS ≤ 2) and unfavorable (mRS > 2) outcomes.
Results: Key findings highlight coronary heart
disease, platelet-to-lymphocyte ratio, uric acid, glucose levels,
homocysteine, and radiomics features as independent predictors of AIS
outcomes. The clinical-radiomics model achieved a ROC-AUC of 0.940 (95%
CI: 0.912–0.969) in the training set and 0.854 (95% CI: 0.781–0.926) in
the validation set, underscoring its predictive reliability and clinical
utility.
Conclusion: The study underscores the efficacy of
the clinical-radiomics model in forecasting AIS prognosis, showcasing
the pivotal role of artificial intelligence in fostering personalized
treatment plans and enhancing patient care. This innovative approach
promises to revolutionize AIS management, offering a significant leap
toward more individualized and effective healthcare solutions.
Highlights
– High predictive accuracy for AIS prognosis.
– Integrates MRI radiomics with clinical factors.
– Utilizes advanced machine learning techniques.
– Provides a validated clinical-radiomics nomogram.
– Facilitates personalized AIS management.
