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Updated: Jun 9, 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

Hypothesis Generation via Interpretable Machine Learning: A Case Study on Risk Factors for Postradiation Therapy Lung

Zheng Zhang¹, Sang Ho Lee², Rich Caruana^3,4

¹Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.

Advances in Radiation Oncology

|June 8, 2026

Summary

This summary is machine-generated.

Explainable Boosting Machine (EBM) shows promise for generating hypotheses in early-stage lung cancer research, despite modest performance on limited data. Its interpretable nature aids in identifying prognostic factors and potential biases.

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Last Updated: Jun 9, 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:

Oncology
Machine Learning
Biostatistics

Background:

Interpretability is crucial for trustworthy oncologic outcome prediction, especially with limited data.
Existing methods often rely on post-hoc explanations for black-box models.
Explainable Boosting Machine (EBM) offers an intrinsically interpretable 'glass-box' alternative.

Purpose of the Study:

Investigate the utility of EBM for hypothesis generation in early-stage lung cancer.
Assess EBM's ability to identify prognostic features and potential biases.
Compare EBM's performance and interpretability against traditional models.

Main Methods:

Applied EBM to a lung cancer recurrence dataset post-radiation therapy.
Compared EBM-identified features with univariate analysis results.
Benchmarked EBM against logistic regression and random forest models.

Main Results:

EBM identified primary tumor size and BMI as key prognostic features, consistent with univariate analysis.
The model revealed potential age bias and race-BMI interaction confounding.
EBM provided competitive performance and enhanced interpretability over logistic regression and random forest.

Conclusions:

EBM's interpretability makes it valuable for hypothesis generation in limited-data oncology settings.
Modest performance currently limits its use as a clinical decision support tool.
Further research is needed to address generalizability challenges with small datasets.