Optimizing normal tissue sparing via spatiotemporal optimization under equivalent tumor-radical efficacy.

Area of Science:

• Radiation Oncology
• Medical Physics
• Computational Biology

Background:

• Traditional radiation therapy optimization maximizes tumor dose but may compromise tumor kill.
• Spatiotemporal optimization balances dose delivery (fractions) and dose per fraction.
• Organs-at-risk (OAR) constraints are crucial to minimize toxicity.

Purpose of the Study:

• To develop a spatiotemporal optimization model for radiation therapy.
• To ensure sufficient biologically effective dose (BED) for tumor cell kill.
• To minimize BED delivered to OARs, considering tumor proliferation dynamics.

Main Methods:

• A novel spatiotemporal optimization model was formulated with inequality constraints.
• The model incorporates tumor lag time and doubling time for dose fractionation optimization.
• Implementation was performed for proton therapy, evaluating performance across various proliferation parameters.

Main Results:

• The model consistently achieved the minimum required BED for tumor cell kill.
• Mean BED to OARs varied with tumor proliferation dynamics (lag and doubling times).
• Optimal fractionation strategies were identified, differing for prostate (e.g., femoral head) and head-and-neck cases.

Conclusions:

• The proposed model effectively minimizes OAR dose while ensuring adequate tumor dose.
• Incorporating tumor growth kinetics (lag and doubling times) optimizes fractionation.
• This versatile model can support advanced fractionation strategies like hyperfractionation and accelerated fractionation.