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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Machine learning approach for hemorrhagic transformation prediction: Capturing predictors' interaction.

Ahmed F Elsaid¹, Rasha M Fahmi², Nahed Shehta²

¹Department of Public Health and Community Medicine, Zagazig University, Zagazig, Egypt.

Frontiers in Neurology

|December 12, 2022

Summary

This summary is machine-generated.

Hemorrhagic transformation (HT) occurs in 19.8% of ischemic stroke patients. Infarction size, cerebral microbleeds, and NIHSS are key predictors, with Random Forest and Gradient Boosting models showing superior prediction accuracy.

Keywords:

NIHSS cerebral microbleeds hemorrhagic transformation infarction size ischemic stroke machine learning

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Area of Science:

Neurology
Medical Imaging
Machine Learning in Medicine

Background:

Hemorrhagic transformation (HT) is a common complication of ischemic stroke.
HT can negatively impact patient prognosis and outcomes.
Predicting and understanding HT is crucial for stroke management.

Purpose of the Study:

To determine the incidence of HT in ischemic stroke patients.
To identify key predictors of HT.
To evaluate and compare the performance of various machine learning models in predicting HT and analyze predictor interactions.

Main Methods:

A prospective study of 354 ischemic stroke patients.
Utilized MRI for HT detection and predictor identification.
Employed machine learning algorithms (LRC, SVC, RFC, GBC, MLPC) for prediction model development and validation.
Investigated predictor interactions using generalized additive modeling (GAM).

Main Results:

The incidence of HT was 19.8%.
Infarction size, cerebral microbleeds (CMB), and NIHSS were significant predictors of HT.
RFC and GBC models achieved the highest predictive performance (AUC: 0.91).
Significant linear and non-linear interactions were found between NIHSS, CMB, and infarction size.

Conclusions:

Infarction size, CMB, and NIHSS are key predictors of HT in ischemic stroke.
RFC and GBC models effectively capture non-linear predictor interactions for improved HT prediction.
Predictor interactions suggest dynamic risk assessment rather than fixed cutoffs for HT.