Optimization of the dose-volume effect parameter "a" in EUD-based TCP models for breast cancer radiotherapy

Authors

Farshid Mahmoudi, School of Allied Medical Sciences, Lorestan University of Medical Sciences, Khorramabad, Iran.
Nahid Chegeni, Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Ali Bagheri, Department of Radiation Oncology, Faculty of Medicine, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Amir Danyaei, Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Samira Razzaghi, Department of Radiation Oncology, Faculty of Medicine, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Shole Arvandi, Department of Radiation Oncology, Faculty of Medicine, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Amal Saki Malehi, Pain Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Bahare Arjmand, Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Azin Shamsi, Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Majid Mohiuddin, Advocate Health - MidwestFollow

Recommended Citation

Mahmoudi F, Chegeni N, Bagheri A, et al. Optimization of the Dose-Volume Effect Parameter "a" in EUD-Based TCP Models for Breast Cancer Radiotherapy. Technol Cancer Res Treat. 2025;24:15330338251329103. doi:10.1177/15330338251329103

Affiliations

Advocate Lutheran General Hospital

Abstract

Introduction: Radiotherapy treatment plans traditionally rely on physical indices like Dose-volume histograms and spatial dose distributions. While these metrics assess dose delivery, they lack consideration for the biological effects on tumors and healthy tissues. To address this, radiobiological models like tumor control probability (TCP) and Normal tissue complications probability (NTCP) are increasingly incorporated to evaluate treatment efficacy and potential complications. This study aimed to assess the predictive power of radiobiological models for TCP in breast cancer radiotherapy and provide insights into the model selection and parameter optimization.

Methods: In this retrospective observational study, two commonly used models, the Linear-Poisson and Equivalent uniform dose (EUD)-based models, were employed to calculate TCP for 30 patients. Different radiobiological parameter sets were investigated, including established sets from literature (G1 and G2) and set with an optimized "a" parameter derived from clinical trial data (a1 and a2). Model predictions were compared with clinical outcomes from the START trials.

Results: The Linear-Poisson model with es lished parameter sets from the literature demonstrated good agreement with clinical data. The standard EUD-based model (a = -7.2) significantly underestimated TCP. While both models exhibited some level of independence from the specific parameter sets (G1 vs. G2), the EUD-based model was susceptible to the "a" parameter value. Optimization suggests a more accurate "a" value closer to -2.57 and -5.65.

Conclusion: This study emphasizes the importance of clinically relevant radiobiological parameters for accurate TCP prediction and optimizing the "a" parameter in the EUD-based model based on clinical data (a1 and a2) improved its predictive accuracy significantly.

Document Type

Article

PubMed ID

40165476