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

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Published on: October 6, 2023

Real-Time AI-Based Radiotherapy Planning for Nasopharyngeal Carcinoma: Development and Validation.

Guangyu Wang¹, Xin Yang¹, Qianxi Ni²

¹State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China.

Cyborg and Bionic Systems (Washington, D.C.)

|May 20, 2026

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Summary

An AI model enables rapid, high-quality radiotherapy planning for nasopharyngeal carcinoma within online all-in-one workflows. This approach demonstrates real-time feasibility and clinical acceptance, advancing precision radiotherapy.

Area of Science:

Radiotherapy
Medical Physics
Artificial Intelligence in Oncology

Background:

Online all-in-one (AIO) radiotherapy workflows integrate simulation, planning, and delivery for same-day treatment.
Generating high-quality plans for complex tumors like nasopharyngeal carcinoma (NPC) within strict time limits is a challenge for AIO adoption.

Purpose of the Study:

To develop and validate a deep-learning-based automated planning model for real-time NPC treatment planning in online AIO workflows.

Main Methods:

A deep learning model was trained on 890 patients, refined through 4 versions with innovations like quantile loss and GPU acceleration.
Retrospective benchmarking across 5 centers (245 patients) and prospective validation on 242 NPC patients using a CT-linear accelerator AIO platform.

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Last Updated: May 21, 2026

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

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Published on: October 6, 2023

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

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

Published on: April 11, 2018

Main Results:

AI-generated plans showed superior or comparable dosimetric quality to expert plans in retrospective evaluation.
In prospective validation, 95% of plans were clinically accepted after one optimization cycle (mean time: 3.5 min).
All prospective plans met coverage criteria and passed secondary verification and in vivo analysis.

Conclusions:

This study provides the largest prospective validation of AI-based treatment planning for NPC, confirming real-time feasibility, generalizability, and clinical quality.
The framework supports scalable adoption of AI-driven precision planning and offers a transferable model for intelligent radiotherapy across various cancer sites.