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Coverage-based treatment planning: optimizing the IMRT PTV to meet a CTV coverage criterion.
1Department of Radiation Oncology, Virginia Commonwealth University, P.O. Box 980058, Richmond, Virginia 23298, USA. jgordon@mcvh-vcu.edu
Medical Physics
|April 22, 2009
Summary
Coverage-based treatment planning iteratively adjusts margins to ensure target coverage despite setup errors. This method reduces planning target volume and normal tissue dose, improving treatment precision.
Area of Science:
- Radiation Oncology
- Medical Physics
- Radiotherapy Planning
Background:
- Ensuring adequate target coverage in radiotherapy is challenged by patient setup errors.
- Current planning methods may not fully account for dosimetric margins and geometric uncertainties.
- Optimizing planning target volume (PTV) margins is crucial for dose escalation and normal tissue sparing.
Purpose of the Study:
- To introduce and evaluate an iterative coverage-based treatment planning approach.
- To assess the method's ability to ensure specified clinical target volume (CTV) coverage under setup uncertainties.
- To quantify the impact of this approach on PTV size and irradiated normal tissue volumes.
Main Methods:
- An iterative algorithm adjusted the clinical target volume to planning target volume (CTV-to-PTV) margin.
- The adjustment continued until a predefined CTV coverage percentage was achieved for a given level of setup error.
- The method was applied to 27 prostate cancer treatment plans.
Main Results:
- The average CTV-to-PTV margin was reduced from 5 mm to 2.8 mm.
- The total volume of normal tissue receiving > or =65 Gy decreased by an average of 19.3% (approx. 48 cc).
- Significant reductions were observed in the irradiated volumes of bladder and periprostatic rectum.
Conclusions:
- Coverage-based treatment planning offers an effective strategy for optimizing radiotherapy plans.
- The iterative approach automatically accounts for dosimetric margins, leading to smaller PTVs and reduced normal tissue toxicity.
- This method advances probabilistic treatment planning and is applicable across various sites and delivery techniques.

