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Updated: Jul 12, 2026

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

Published on: October 6, 2023

Artificial Intelligence-Powered Radiotherapy for Resource-Limited Settings: Advancing Cervical and Prostate Cancer

Tucker J Netherton¹, Ajay Aggarwal^2,3, Qusai Alakayleh¹

¹Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.

JCO Global Oncology

|July 9, 2026

Summary

This study developed an AI-powered Radiation Planning Assistant (RPA) for automated radiotherapy planning in prostate and cervical cancers. The RPA significantly improves efficiency and consistency, enhancing accessibility for low-resource settings.

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Last Updated: Jul 12, 2026

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

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Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

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

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Area of Science:

Medical Physics
Radiation Oncology
Artificial Intelligence in Healthcare

Background:

Radiotherapy treatment planning is complex, manual, and prone to delays and variability.
The Radiation Planning Assistant (RPA) was developed to automate contouring and planning, especially for low-resource settings.

Purpose of the Study:

To develop and clinically validate end-to-end AI-driven workflows for prostate and cervical cancer radiotherapy planning using the RPA.
To enhance efficiency, consistency, and accessibility of radiotherapy planning in low- and middle-income countries.

Main Methods:

Developed deep learning auto-contouring models (nnU-Net) integrated with knowledge-based planning.
Trained models on over 1,000 prostate and 110 cervical cancer treatment plans.
Assessed clinical acceptability of auto-contours and plans retrospectively by radiation oncologists.

Main Results:

70-80% of auto-contours and 73-80% of treatment plans were clinically acceptable without edits for prostate and cervical cancers, respectively.
Prostate cancer planning met 77% target and 98% organ-at-risk compliance; cervical cancer met all EMBRACE II protocol hard constraints.
While some contours (bowel, vaginal) had lower performance, plan quality was not compromised.

Conclusions:

Validated end-to-end radiotherapy planning workflows for prostate and cervical cancers are presented.
The RPA streamlines treatment planning in a globally accessible platform, demonstrating high clinical acceptability.
AI-driven automation offers a promising solution for improving radiotherapy access and quality worldwide.