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Updated: Sep 1, 2025

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

Improving Proton Dose Calculation Accuracy by Using Deep Learning.

Chao Wu^1,2,3, Dan Nguyen¹, Yixun Xing¹

¹Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America.

Machine Learning: Science and Technology

|August 15, 2022

Summary

This summary is machine-generated.

A new deep learning model converts fast but inaccurate pencil beam (PB) dose calculations to accurate Monte Carlo (MC) dose calculations. This AI-driven approach enhances proton therapy planning by improving dose accuracy for various tumor sites.

Keywords:

Deep learning Monte Carlo Pencil beam Proton dose calculation

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Published on: October 10, 2018

Area of Science:

Medical Physics
Computational Biology
Radiotherapy

Background:

Pencil beam (PB) dose calculation offers speed but lacks accuracy in heterogeneous environments.
Monte Carlo (MC) simulation provides high accuracy but is computationally intensive.
Accurate dose calculation is critical for effective radiotherapy planning.

Purpose of the Study:

To develop a deep learning model for converting PB dose calculations to MC-level accuracy.
To enhance the precision of dose calculations across diverse tumor sites in proton therapy.

Main Methods:

A deep learning model was trained using PB dose and CT images as input to generate MC dose distributions.
The model was trained and validated on a dataset of 290 patients across head and neck, liver, lung, and prostate cancer sites.
Numerical experiments explored optimal training dataset combinations for various tumor sites.

Main Results:

The best model performance was achieved when trained on combined data from all tumor sites, using individual beam dose distributions as input.
Average gamma passing rates (1mm/1%) reached 92.8% (head and neck), 92.7% (liver), 89.7% (lung), and 99.6% (prostate).
Dose conversion averaged under 4 seconds per field, with successful adaptation via transfer learning.

Conclusions:

Deep learning effectively enhances PB dose calculation accuracy to MC levels.
The developed model can be integrated into clinical proton therapy workflows to improve treatment planning and dose accuracy.