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Related Experiment Video

Updated: May 14, 2026

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

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

Published on: October 10, 2018

Individualized Treatment Effect Inference of Head and Neck Cancer with Multimodal Data.

Yawen Wei^1,2, Zhen Li¹, Jonghye Woo³

¹Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT.

APSIPA Transactions on Signal and Information Processing

|May 13, 2026

Summary

This summary is machine-generated.

This study introduces a deep learning framework for estimating individualized treatment effects in head and neck cancer patients using multimodal data. The model significantly reduces bias, improving causal effect estimation for personalized medicine.

Keywords:

Causal Inference Deep Learning Head and Neck Cancer Individualized Treatment Effect Medical Imaging Mutual Information

Related Experiment Videos

Last Updated: May 14, 2026

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

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

Published on: October 10, 2018

Area of Science:

Artificial Intelligence
Medical Informatics
Oncology

Background:

Estimating individualized treatment effects (ITE) is crucial for personalized medicine but challenged by selection bias in observational data and complex multimodal patient information.
Existing methods like adversarial training struggle with instability when applied to multimodal data and multiple treatment options.

Purpose of the Study:

To develop an end-to-end deep learning (DL) framework for accurate ITE inference using multimodal data in head and neck cancer (HNC).
To mitigate treatment selection bias and improve causal effect estimation for personalized treatment decisions.

Main Methods:

Developed a DL framework incorporating multimodal patient data (CT scans, clinical variables) and multiple treatments.
Proposed bi-stage adaptive instance normalization (Bi-AdaIN) for efficient, robust information fusion, handling missing values.
Utilized mutual information (MI) regularization to disentangle treatment and status features for accurate factual and counterfactual outcome predictions.

Main Results:

Evaluated on the RADCURE dataset (3,346 HNC cases).
The proposed model substantially reduced Bias-Adjusted Treatment Effect (BATE) compared to conventional methods and adversarial training.
Demonstrated more robust estimation of causal effects.

Conclusions:

This work presents one of the first DL-based approaches for ITE estimation using multimodal medical imaging.
Offers a promising method for counterfactual reasoning in clinical oncology to support decision-making.