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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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Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and

Musa Abdulkareem^1,2,3, Asmaa A Kenawy^1,2, Elisa Rauseo^1,2

¹Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom.

Frontiers in Cardiovascular Medicine

|September 2, 2022

Summary

This study explored predicting myocardial infarction (MI) using only pre-contrast cardiac magnetic resonance (CMR) images. While initial results are modest, it shows promise for non-contrast MI assessment.

Keywords:

CMR contrast contrast-free decision tree deep learning machine learning support vector machines

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MRI and PET in Mouse Models of Myocardial Infarction

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Published on: December 19, 2013

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Cardiovascular Disease Diagnostics

Background:

Contrast agents are currently essential for accurate myocardial infarction (MI) visualization and quantification via cardiac magnetic resonance (CMR).
Developing non-contrast methods for MI assessment could reduce risks and improve patient accessibility.

Purpose of the Study:

To investigate the feasibility of predicting post-contrast CMR information using only pre-contrast images.
To analyze pre- and post-contrast CMR images to identify predictive features for MI.

Main Methods:

Utilized a dataset of 272 CMR studies (108 MI, 164 controls).
Employed deep learning (UNet for segmentation, ResNet50 for classification) and traditional machine learning (SVM, DT) on pre-contrast cine short-axis images.
Incorporated optical flow, myocardial area change rate, and radiomics for feature extraction.

Main Results:

The UNet segmentation model achieved mean Dice scores of 0.75 (endocardium), 0.51 (epicardium), and 0.20 (scar).
Classification models showed modest performance: SVM achieved 0.68 accuracy, 0.69 F1, and 0.64 precision; DT achieved 0.62 accuracy, 0.63 F1, and 0.72 precision.

Conclusions:

Promising approaches using deep learning and machine learning were presented for predicting contrast-enhanced information from non-contrast CMR images.
Despite modest initial results, this research highlights open problems and potential for non-contrast MI assessment.