# Leveraging deep learning for markerless motion management in radiation therapy

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $424,249

## Abstract

Leveraging deep learning for markerless motion management in radiation therapy
Project Summary
Organ motion is a predominant limiting factor for the maximum exploitation of modern radiation therapy
(RT). Adverse influence of the organ motion is aggravated in hypofractionated treatment because of
protracted dose delivery. Current image guided RT often relies on the use of implanted fiducial markers
(FMs) for online/offline target localization, which is invasive and costly, and introduces possible
bleeding, infection and discomfort of the patient. In this project, we harness the enormous potential of
deep learning and investigate a novel markerless localization strategy by combined use of a pre-trained
deep learning model and kV X-ray projection or cone beam CT images. We hypothesize that incorporation
of deep layers of image information allows us to visualize otherwise invisible target in real-time and greatly
reduce the uncertainties in beam targeting. Specific aims of the project are to: (1) Develop a DL-based
tumor target localization framework for image guided RT (IGRT); (2) Apply the DL-based strategy to
localize prostate target on 2D kV X-ray projection and 3D CBCT images; and (3) Evaluate the potential
clinical impact of the DL strategy for pancreatic IGRT. This study brings up, for the first time, highly
accurate markerless target localization based on deep learning and provides a clinically sensible solution
for IGRT of prostate and pancreas cancers or other types of cancers. Successful completion of this
investigation will significantly advance the current beam targeting technique and provide radiation
oncology discipline a powerful way to safely and reliably escalate the radiation dose for precision RT.
Given its significant promise to optimally cater for inter- and intra-fractional uncertainties, the study
should lead to substantial improvement in patient care and enables us to utilize maximally the technical
capability of modern RT such as IMRT and VMAT. Given the dose responsive nature of various cancers and
that the proposed method requires no hardware modification, this research should lead to a widespread impact
on the management of neoplasmic diseases affected by organ motion.

## Key facts

- **NIH application ID:** 10374171
- **Project number:** 5R01CA256890-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Lei Xing
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $424,249
- **Award type:** 5
- **Project period:** 2021-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10374171

## Citation

> US National Institutes of Health, RePORTER application 10374171, Leveraging deep learning for markerless motion management in radiation therapy (5R01CA256890-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10374171. Licensed CC0.

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