# Ionization Detail - Biologically based treatment planning for particle therapy beyond LET-RBE

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $581,657

## Abstract

Project Summary
Current proton and ion therapy treatment planning procedures utilize either the physical quantity linear
energy transfer (LET) as a surrogate for biological effectiveness or make use of relative biological
effectiveness (RBE) models that convert absorbed dose to biologically weighted dose, assumed to be iso-
effective to photons. LET is indeed important clinically for planning treatments with charged particles, but
there are known problems. Ion beams with the same LET can have different RBE, depending on particle type
and energy. Therefore, LET by itself is not an ideal parameter to use in radiation treatment planning (RTP).
For clinical application of carbon therapy, RBE-models have been developed. However, comparisons of
different RBE models used for carbon therapy have shown that dose prescriptions implemented with the
European local effect model or the Japanese National Institute of Radiological Sciences mixed beam model
can be up to 15% different. We use the term ionization detail (ID) to mean the detailed distribution of
ionizing events along a particle track on the nanometer scale. Our chief hypothesis, which is
supported by strong prior evidence, is that ID can predict, better than LET and existing RBE
models, the biological effects associated with high-LET radiation. We have previously shown how
ID can be used together with these models to improve their performance, providing a path for integrating
ID-based RTP into clinical practice. Our approach could lead to a consensus in proton and ion therapy RTP.
With four Specific Aims, we have chosen a translational and stepwise approach to build an ID-based
prediction model. We will test this model for different endpoints and model systems ranging from in vitro
cell and molecular data, obtained by irradiating human cancer cells in flasks and anatomical phantoms, to
in vivo mice/human tumor data. We will develop advanced algorithms and computational GPU-
based methods and use them for effective inverse treatment planning with actively scanned proton and ion
beams. This technology will be applied to demonstrate the practicality and evaluate the clinical efficacy of our
approach in prostate and chordoma treatments, first prospectively in human-size pelvis and head phantoms,
and finally, retrospectively in patients treated for these diseases. We have assembled a strong team with the
complementary expertise needed for this project. Members of our team have all successfully
collaborated together. Upon completion, we will provide a rigorously tested and validated approach to
ID-based particle RTP that will be available for cross-correlation with existing clinical data and for
careful testing in prospective clinical particle therapy trials.

## Key facts

- **NIH application ID:** 10689288
- **Project number:** 5R01CA266467-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** BRUCE FADDEGON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $581,657
- **Award type:** 5
- **Project period:** 2022-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10689288

## Citation

> US National Institutes of Health, RePORTER application 10689288, Ionization Detail - Biologically based treatment planning for particle therapy beyond LET-RBE (5R01CA266467-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10689288. Licensed CC0.

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