Machine learning-based nomogram: integrating MRI radiomics and clinical indicators for prognostic assessment in acute ischemic stroke

 So a triple failure; My god, the mentors and senior researchers need remedial education in the point of stroke research! Getting survivors recovered!

1. Predicting failure to recover.

2. 'Assessments'

3. Prognostication
   

Machine learning-based nomogram: integrating MRI radiomics and clinical indicators for prognostic assessment in acute ischemic stroke

Kun Guo¹Bo Zhu¹Rong Li¹Jing Xi¹Qi Wang²KongBo Chen³Yuan Shao³Jiaqi Liu³Weili Cao¹Zhiqin Liu¹Zhengli Di¹Naibing Gu^1*

• 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.