# Development of AI-Augmented quality assurance tools for radiation therapy

> **NIH NIH R01** · STANFORD UNIVERSITY · 2023 · $550,091

## Abstract

Development of AI-Augmented quality assurance tools for radiation therapy
Project summary
Quality assurance (QA) is an essential part of radiation therapy (RT) workflow and critically determines
the success of patient care. However, current treatment plan QA methods and tools are deficient in
multiple aspects and suffer from problems such as limited accountability, labor intensive, and costly. In
this project we will leverage the emerging deep learning techniques to create clinically translatable
solutions for robust and efficient QA of modern RT. Specifically, we aim to (i) establish a novel
framework for using deep learning to verify the machine delivery parameters of an RT treatment plan; (ii)
investigate the use of deep learning for RT dose verification; and (iii) evaluate the performance of the QA
system and show its potential clinical impact
. This research presents the first-of-its-kind treatment plan
QA strategy capable of providing both machine delivery parameters (MLC apertures and MUs of the
involved IMRT/VMAT fields) and dosimetric distribution on the patient’s treatment geometry. The
research will also make it possible to take advantage of the useful features of both deep learning models
from Aims 1 and 2 and check the cycle-consistency of a treatment plan (i.e., from the beam parameters of
the plan to the corresponding 3D dose distribution, and then from the 3D dose to the beam parameters)
for enhanced plan QA. Successful completion of the project will provide urgently needed plan QA tools
for safe, efficient and high-quality RT practice, and enable patients to truly benefit from modern RT
modalities. Finally, the proposed strategy is quite broad and can be readily generalized for QA of other
treatment modalities, such as proton therapy and high-dose rate (HDR) brachytherapy.

## Key facts

- **NIH application ID:** 10558155
- **Project number:** 1R01CA275772-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Lei Xing
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $550,091
- **Award type:** 1
- **Project period:** 2023-02-01 → 2028-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10558155

## Citation

> US National Institutes of Health, RePORTER application 10558155, Development of AI-Augmented quality assurance tools for radiation therapy (1R01CA275772-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10558155. Licensed CC0.

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