An extended local effect model for Auger-emitting radionuclide therapy.
Tae Wan Kim1,2, Chang-Min Lee1, Taeyun Kim1
1Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.
Physics in Medicine and Biology
|May 6, 2026
Summary
This study extends the local effect model (LEM) to predict radiobiological effects from Auger-emitting radionuclides, incorporating subcellular dose distributions for improved cancer therapy. The model accurately predicts cell survival and tumor control probability, crucial for radionuclide therapy (RNT).
Area of Science:
- Medical Physics
- Radiation Oncology
- Nuclear Medicine
Background:
- Auger-emitting radionuclide therapy (RNT) effectiveness depends on precise spatial and temporal dose delivery at the subcellular level.
- Existing models require enhancement to accurately predict biological outcomes from complex dose distributions.
Purpose of the Study:
- To extend the local effect model (LEM) for predicting radiobiological effects of Auger-emitting radionuclides.
- To integrate nanometer-scale dose distributions and cell-specific DNA repair kinetics into a unified model.
Main Methods:
- Utilized Geant4-DNA to calculate dose point kernels for 103Pd, 111In, and 125I.
- Integrated dose kernels into an extended LEM with Monte Carlo simulations (GLOBLE model) for kinetic analysis.
- Validated the model against in vitro experimental data for 111In.
Main Results:
- The extended LEM accurately predicts cell survival fractions, validated by 111In data.
- Quantified relative biological effectiveness (RBE) dependence on subcellular localization, with RBE10 up to 4.19 for intranuclear 125I.
- Model captures dose-rate effects and predicts significant variations in tumor control probability (TCP) based on radionuclide and uptake.
Conclusions:
- Established an extended LEM as a mechanistic platform for evaluating Auger RNT.
- The model integrates nanodosimetry with DNA damage and repair kinetics for improved treatment prediction.
- Provides a framework for optimizing radionuclide selection and targeting strategies in RNT.
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