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SU-F-T-345: Quasi-Dead Beams: Clinical Relevance and Implications for Automatic Planning.

Medical Physics 2016 June
PURPOSE: Beam direction selection for fixed-beam IMRT planning is typically a manual process. Severe dose-volume limits on critical structures in the thorax often result in atypical selection of beam directions as compared to other body sites. This work demonstrates the potential consequences as well as clinical relevance.

METHODS: 21 thoracic cases treated with 5-7 beam directions, 6 cases including non-coplanar arrangements, with fractional doses of 150-411cGy were analyzed. Endpoints included per-beam modulation scaling factor (MSF), variation from equal weighting, and delivery QA passing rate.

RESULTS: During analysis of patient-specific delivery QA a sub-standard passing rate was found for a single 5-field plan (90.48% of pixels evaluated passing 3% dose, 3mm DTA). During investigation it was found that a single beam demonstrated a MSF of 34.7 and contributed only 2.7% to the mean dose of the target. In addition, the variation from equal weighting for this beam was 17.3% absolute resulting in another beam with a MSF of 4.6 contributing 41.9% to the mean dose to the target; a variation of 21.9% from equal weighting. The average MSF for the remaining 20 cases was 4.0 (SD 1.8) with an average absolute deviation of 2.8% from equal weighting (SD 3.1%).

CONCLUSION: Optimization in commercial treatment planning systems typically results in relatively equally weighted beams. Extreme variation from this can result in excessively high MSFs (very small segments) and potential decreases in agreement between planned and delivered dose distributions. In addition, the resultant beam may contribute minimal dose to the target (quasi-dead beam); a byproduct being increased treatment time and associated localization uncertainties. Potential ramifications exist for automatic planning algorithms should they allow for user-defined beam directions. Additionally, these quasi-dead beams may be embedded in the libraries for model-based systems potentially resulting in inefficient and less accurate deliveries.

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