Adaptive treatment-length optimization in spatiobiologically integrated radiotherapy

Area of Science:

• Radiation Oncology
• Medical Physics
• Computational Biology

Background:

• Current radiotherapy planning often uses fixed treatment durations.
• Adapting fluence-maps to tumor changes is an active area of research.
• Quantitative imaging provides insights into tumor evolution during treatment.

Purpose of the Study:

• To develop an optimization model for adaptive radiotherapy that adjusts both treatment course length and fluence-maps.
• To minimize the total number of tumor cells remaining (TNTCR) at the end of treatment.
• To ensure biologically effective dose limits for organs-at-risk are met.

Main Methods:

• A convex optimization model was developed to determine optimal treatment duration and fluence-maps.
• The model uses tumor cell density information from functional imaging at each session.
• Treatment plans were re-optimized iteratively based on updated tumor status.

Main Results:

• Simulations on head-and-neck cancer cases showed reduced TNTCR compared to adaptive fluence-maps alone.
• The proposed method increased the biological effect on the tumor.
• Adaptive treatment length and fluence-map planning resulted in shorter overall treatment courses.

Conclusions:

• Adaptive radiotherapy planning that incorporates treatment length optimization is feasible and effective.
• This approach offers improved tumor control and treatment efficiency.
• Dynamic adjustment of treatment parameters based on imaging is superior to fixed-course strategies.