Estimating dose painting effects in radiotherapy: a mathematical model

Area of Science:

• Oncology
• Radiation Oncology
• Computational Biology

Background:

• Tumor heterogeneity is a key factor in cancer progression and treatment resistance.
• Current non-invasive methods cannot adequately assess tumor heterogeneity.
• Radiotherapy often assumes a homogeneous tumor, potentially leading to suboptimal treatment.

Purpose of the Study:

• To investigate the impact of radiation dose distribution on heterogeneous tumors using a computational model.
• To explore how differences in cell cycle duration and radiosensitivity influence tumor cell spatial distribution.
• To evaluate the efficacy of heterogeneous vs. homogeneous radiation dosimetry for tumor control.

Main Methods:

• Development of an individual cell-based model to simulate tumor growth and response to radiation.
• Inclusion of two distinct tumor cell phenotypes: ordinary cancer cells (CCs) and cancer stem cells (CSCs).
• Simulation of varying cell cycle durations and radiosensitivity between CCs and CSCs.

Main Results:

• Tumor growth leads to spatial segregation of phenotypes, with longer cell cycle CSCs concentrating internally.
• Heterogeneous dosimetry, boosting radiation to radioresistant CSCs, enhances tumor control.
• Simulated heterogeneous dose distributions consistently outperformed homogeneous distributions.

Conclusions:

• Tumor heterogeneity significantly influences treatment outcomes in radiotherapy.
• Targeted, heterogeneous radiation doses can improve efficacy by focusing on radioresistant cancer stem cells.
• Computational modeling provides insights into optimizing radiotherapy for heterogeneous tumors.