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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

Incorporating auxiliary information for improved prediction using combination of kernel machines.

Xiang Zhan¹, Debashis Ghosh²

¹Department of Statistics, Pennsylvania State University, University Park, PA 16802, U.S.A.

Statistical Methodology

|November 25, 2014

Summary

This study introduces a kernel machine method to improve prediction models using auxiliary genomic data. The hybrid approach enhances predictive accuracy for biological outcomes, validated in lung cancer and Alzheimer

Keywords:

Auxiliary information Combination of kernels Hybrid predictor Kernel ridge regression Mean squared prediction error

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Last Updated: Apr 20, 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:

Genomics
Biostatistics
Computational Biology

Background:

Genomic technologies provide multiple measures for biological phenomena.
Accurate prediction models are crucial for understanding disease.
Auxiliary information can potentially improve existing prediction models.

Purpose of the Study:

To develop a kernel machine-based method for improving prediction of an outcome variable Y using covariates X.
To incorporate surrogate covariates W, related to X, to boost prediction accuracy.
To propose a hybrid kernel machine predictor combining single kernel machines for reduced prediction error.

Main Methods:

Kernel machine-based regression.
Incorporation of auxiliary information (surrogate covariates W).
Hybrid kernel machine construction by combining single kernel machines.

Main Results:

The proposed kernel machine method improves prediction of Y by X using W.
The hybrid kernel machine predictor yields smaller prediction errors than individual constituent predictors.
Simulations demonstrate the effectiveness of the proposed methods.

Conclusions:

Kernel machine approaches effectively leverage auxiliary genomic data for enhanced prediction.
Hybrid models offer improved predictive performance compared to single models.
The method shows promise for applications in complex diseases like lung cancer and Alzheimer's.