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

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Applications of Machine Learning for Radiation Therapy.

Hidetaka Arimura¹, Takahiro Nakamoto²

¹Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University.

Igaku Butsuri : Nihon Igaku Butsuri Gakkai Kikanshi = Japanese Journal of Medical Physics : an Official Journal of Japan Society of Medical Physics

|April 22, 2017

Summary

Machine learning enhances radiation therapy by improving tumor volume estimation and predicting treatment side effects. This review explores AI applications in image-guided radiation therapy (IGRT) and highlights key considerations for implementing these advanced techniques.

Keywords:

image guided radiation therapy machine learning outcome prediction

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Area of Science:

Medical Physics
Radiotherapy
Artificial Intelligence

Background:

Image-guided radiation therapy (IGRT) leverages image engineering for advanced cancer treatment.
Machine learning (ML) and image engineering are increasingly integrated to refine radiation therapy protocols.

Purpose of the Study:

To review current research on machine learning applications in radiation therapy.
To highlight specific ML methods for improving treatment accuracy and predicting patient outcomes.

Main Methods:

Review of ML-based methods for Standardized Uptake Value (SUV) threshold determination in Positron Emission Tomography (PET) for Gross Tumor Volume (GTV) estimation.
Analysis of ML approaches for estimating Multileaf Collimator (MLC) position errors during radiation delivery.
Examination of ML frameworks for predicting esophageal stenosis and radiation pneumonitis risk post-therapy.

Main Results:

ML aids in precise GTV delineation using PET-SUV thresholds.
ML can identify and correct MLC position errors in real-time.
ML models show promise in predicting adverse events like radiation pneumonitis.

Conclusions:

Machine learning offers significant potential to advance radiation therapy precision and patient safety.
Further research and careful consideration of implementation challenges are necessary for widespread ML adoption in IGRT.
The review identifies seven critical issues for applying ML models in clinical radiation oncology settings.