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Predictive Immune Modeling of Solid Tumors

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Multimodal Fusion Artificial Intelligence Model to Predict Risk for MACE and Myocarditis in Cancer Patients Receiving

Chadi Ayoub¹, Lalith Appari², Milagros Pereyra¹

¹Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, Arizona, USA.

|January 6, 2025

Summary

This summary is machine-generated.

An artificial intelligence model can predict immune checkpoint inhibitor (ICI)-related myocarditis and cardiovascular events in cancer patients. This AI tool aids in identifying high-risk individuals for closer monitoring during ICI therapy.

Keywords:

artificial intelligence cardiovascular events immune checkpoint inhibitor myocarditis

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Area of Science:

Cardiology
Oncology
Artificial Intelligence

Background:

Immune checkpoint inhibitor (ICI) therapy improves cancer prognosis but carries risks of myocarditis and cardiovascular events.
Cardiovascular adverse events, including myocarditis and mortality, are significant concerns in patients receiving ICI therapy.

Purpose of the Study:

To develop an artificial intelligence (AI) model for predicting ICI-related myocarditis and major adverse cardiovascular events.
To enhance early detection and risk stratification for cardiovascular complications in cancer patients undergoing ICI treatment.

Main Methods:

A multimodal joint fusion AI model was developed using baseline characteristics, laboratory values, and electrocardiogram (ECG) data from cancer patients treated with ICI.
The AI model integrated tabular data and ECG information via 1-D convolution and multilayer perceptron for a comprehensive analysis.
A composite outcome of ICI-related myocarditis and major adverse cardiovascular events was defined for model training and validation.

Main Results:

The developed AI model demonstrated good prognostic performance with an area under the operating characteristics curve of 0.72.
The multimodal fusion model outperformed models using only ECG or baseline electronic medical record data.
In a cohort of 2,258 patients, 11.7% experienced cardiovascular adverse events, including ICI-related myocarditis and major adverse cardiovascular events.

Conclusions:

A multimodal fusion AI model effectively predicts myocarditis and adverse cardiovascular events in cancer patients initiating ICI therapy.
This AI model shows clinical utility in identifying patients at higher risk, potentially enabling closer surveillance and timely intervention.