# A digital twin to track and predict the evolution of pediatric neuroblastoma tumors

> **NIH NIH U54** · DANA-FARBER CANCER INST · 2024 · $249,999

## Abstract

SUMMARY/ABSTRACT
This application is being submitted in response to the Notice of Special Interest (NOSI) identified
as NOT-CA-24-015.
The proposed project aims to pioneer the development of a tumor digital twin, specifically focusing on enhancing
dosimetry and radiation biology within radiopharmaceutical therapy (RPT). By leveraging advanced
computational models, this initiative addresses the significant gap in applying rigorously validated digital twins
for clinical and, notably, radiation oncology purposes. The emergence of RPT as a potent therapeutic option in
cancer care, employing targeted ligands labeled with radioactive elements, presents unique challenges and
opportunities. Unlike conventional external beam radiation therapy (EBRT), RPT molecular nature results in a
heterogeneous dose distribution influenced by the differential expression of targeted receptors across tumors.
Our overarching goal is to construct a comprehensive and validated digital twin platform for neuroblastoma
tumors, encompassing the intricate dynamics of tumor evolution and the specificities of radiopharmaceutical
administration. In close alignment with an ongoing project focused on pediatric neuroblastoma within the U54
ROBIN initiative, this proposal aims to complement and extend existing research by incorporating a hybrid agent-
based model (ABM) capable of simulating tumor dynamics and radiation effects, including the beta emissions
from 131I-MIBG used for these patients. We introduce several novel aspects, including utilizing a hybrid multi-
scale ABM capable of handling clinically relevant tumor sizes, an explicitly modeled interacting vasculature, and
integrating Monte Carlo techniques for state-of-the-art dose calculation. The project is positioned to adapt these
innovations to neuroblastoma, leveraging patient-specific data from clinical trials to inform the digital twin
platform. The approach includes developing the digital twin with a keen focus on tumor heterogeneity and
radiopharmaceutical dynamics and adapting the model to neuroblastoma by incorporating trial data for
calibration and validation. Through these efforts, the project endeavors to create a predictive model that can
simulate individual patient responses to 131I-MIBG therapy, thus offering a translational tool poised to transform
pediatric oncology treatment strategies. This project promises to contribute substantially to the field by achieving
its objectives and providing a scientifically rigorous yet clinically applicable digital twin platform. This platform will
advance our understanding of RPT dosimetry and radiation biology and pave the way for personalized, optimized
cancer treatment strategies, ultimately improving patient outcomes in pediatric oncology.

## Key facts

- **NIH application ID:** 11059442
- **Project number:** 3U54CA274516-02S1
- **Recipient organization:** DANA-FARBER CANCER INST
- **Principal Investigator:** Franziska Michor
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $249,999
- **Award type:** 3
- **Project period:** 2023-09-19 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11059442

## Citation

> US National Institutes of Health, RePORTER application 11059442, A digital twin to track and predict the evolution of pediatric neuroblastoma tumors (3U54CA274516-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11059442. Licensed CC0.

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