# eXtended Modular ANthropomorphic (XMAN) phantom for Imaging and Treatment Optimization in Radiotherapy.

> **NIH NIH R01** · UNIVERSITY OF MARYLAND BALTIMORE · 2022 · $647,722

## Abstract

ABSTRACT
This proposal aims to develop an eXtended Modular ANthropomorphic (XMAN) digital phantom to simulate
realistic patient images with both population-based and individualized motions to establish effective motion
management in liver cancer radiotherapy (RT). With over 70% of liver cancer patients surgically unresectable,
stereotactic body radiation therapy (SBRT) has become an essential non-invasive alternative treatment.
However, despite its promise, liver SBRT has a local failure rate of over 20% at 2-3 years, grade 3+ toxicity
over 20%, and resulted in deaths for over 10% of cirrhotic patients. Previous studies demonstrated strong
associations between the treatment outcome and the RT delivery accuracy. Accuracy of liver SBRT is severely
impaired by patient motion, such as breathing and daily motions. Current practice uses a large planning target
volume (PTV) margin of 5-10mm to account for patient motion, which leads to increased dose to normal
tissues increasing the toxicity and limiting dose escalation to increase tumor control. Lack of effective motion
management is a key barrier to improving outcomes in liver SBRT. Several challenges exist for establishing
effective motion management: (1) Lack of ground-truth (GT) for evaluation. Liver SBRT is mostly guided by
cone-beam CT (CBCT), which has limited tumor visibility. Thus, no GT tumor volume can be defined in CBCT
to evaluate the treatment errors. (2) Lack of effective optimization tools: In patient data, various factors
affecting motion management compound one another and cannot be separated to be optimized individually. (3)
Lack of individualized strategy. The current population-based approach is far from optimal for individual
patients. Digital simulation provides GT images and motion patterns, effective tools for optimization with well-
controlled parameters, and the possibility for patient-specific simulation. However, current phantoms lack
realistic simulation of imaging and motions as well as patient-specific simulation. We will address these
limitations by pursuing the following aims: Aim 1: Establish generic XMAN to simulate patient imaging and
motions in the entire RT workflow. Aim 2: Establish Patient-specific Adaptive XMAN (PAXMAN) to customize
motion management. Aim 3: Translate and apply XMAN and PAXMAN in RT to validate their clinical impact.
The hypothesis is that PTV margin can be reduced to 3-5 mm while ensuring tumor coverage and minimizing
normal tissue dose using XMAN. Deliverables: This project will deliver a suite of XMAN phantoms, the first of
their kind, to simulate realistic patient images and motions in the entire RT workflow, providing essential tools
to evaluate and optimize motion management. PAXMAN unlocks the potential to move from population-based
heuristic approaches into high-precision patient-specific motion management. Our multi-institution and industry
partners have a strong track record of collaboration, ensuring the success of this grant. Overal...

## Key facts

- **NIH application ID:** 10519137
- **Project number:** 1R01EB032680-01A1
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** Lei Ren
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $647,722
- **Award type:** 1
- **Project period:** 2022-09-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10519137

## Citation

> US National Institutes of Health, RePORTER application 10519137, eXtended Modular ANthropomorphic (XMAN) phantom for Imaging and Treatment Optimization in Radiotherapy. (1R01EB032680-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10519137. Licensed CC0.

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