# Early-Stage Clinical Trial of AI-Driven CBCT-Guided Adaptive Radiotherapy for Lung Cancer

> **NIH NIH R21** · EMORY UNIVERSITY · 2024 · $195,625

## Abstract

PROJECT SUMMARY
Stereotactic body radiation therapy (SBRT) is a highly effective treatment for early-stage non-small cell lung
cancer, but its accuracy can be compromised by multiple factors. There is an interval between simulation and
the first day of treatment, the size and position of targets and organs at risk can shift over a course of
treatment, and the thorax is in constant multidimensional motion. Adaptive radiation can improve the accuracy
of SBRT, but implementing it within the workflow of a busy radiation oncology clinic currently requires re-
simulation and re-planning, costing valuable departmental time and resources. Cone beam computed
tomography (CBCT) scans are obtained daily prior to the delivery of each fraction, but their utility for adaptive
radiation therapy has been limited by their image quality. Processing time also remains a significant barrier for
real-time deep learning-based methodologies. The objective of our proposed research is therefore to develop,
validate, and test in an early clinical trial the feasibility of using our two-part cone-beam computed tomography-
based deep learning method for dose verification based on rapid and accurate generation of high quality
synthetic CTs and multi-organ segmentation. In this project, we will pursue two Specific Aims: 1) to develop
and refine CBCT-based synthetic CTs for CBCT quality improvement, and 2) to evaluate the clinical feasibility
of our synthetic CT-based dose verification. The early clinical trial will prospectively enroll patients with early-
stage non-small cell lung cancer receiving definitive SBRT. Validation of the feasibility of this method is a
necessary intermediate step towards our longer-term goal of the implementation of real-time lung cancer
adaptive radiation, which will allow for increased accuracy of higher dose to target volumes and lower doses to
organs at risk, thereby improving local control and decreasing radiation-related risks and toxicities for patients
with non-small cell lung cancer.

## Key facts

- **NIH application ID:** 10828342
- **Project number:** 5R21EB033994-02
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Aparna Kesarwala
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $195,625
- **Award type:** 5
- **Project period:** 2023-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10828342

## Citation

> US National Institutes of Health, RePORTER application 10828342, Early-Stage Clinical Trial of AI-Driven CBCT-Guided Adaptive Radiotherapy for Lung Cancer (5R21EB033994-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10828342. Licensed CC0.

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