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Updated: Jun 29, 2025

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

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A muti-modal feature fusion method based on deep learning for predicting immunotherapy response.

Xiong Li¹, Xuan Feng¹, Juan Zhou¹

¹School of Software, East China Jiaotong University, Nanchang 330013, China.

Journal of Theoretical Biology

|April 8, 2024

Summary

This summary is machine-generated.

Predicting immune checkpoint therapy (ICT) response is crucial. A novel deep learning model, MFMDL, integrates gene networks, pathways, and immune cells, outperforming traditional biomarkers for better cancer treatment prediction.

Keywords:

Data fusion Deep learning Precision medicine Tumor immunotherapy response

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Area of Science:

Oncology
Immunology
Bioinformatics

Background:

Immune checkpoint therapy (ICT) has revolutionized cancer treatment, yet response rates remain limited.
Predicting patient response to ICT is essential for optimizing therapeutic strategies.

Purpose of the Study:

To develop and validate a novel deep learning model for predicting immune checkpoint therapy (ICT) response.
To enhance the accuracy of ICT response prediction by integrating multi-modal biological data.

Main Methods:

Developed a multi-modal feature fusion deep learning (MFMDL) model.
Utilized graph neural networks to represent gene-gene relationships.
Fused gene network embeddings, biological pathway features, and immune cell infiltration data.

Main Results:

The MFMDL model demonstrated superior predictive performance across five diverse cancer datasets (melanoma, lung, gastric).
MFMDL outperformed traditional ICT biomarkers, including ICT targets and tumor microenvironment markers.
Ablation studies confirmed the necessity of multi-modal feature fusion for improved prediction accuracy.

Conclusions:

The MFMDL model offers a robust and accurate approach for predicting ICT response.
Integrating multi-modal data significantly enhances the predictive power for cancer immunotherapy.
This deep learning strategy holds promise for personalized cancer treatment selection.