Program Information

PTV to Skin Proximity for Head and Neck IMRT Treatment Planning

R Price*, S Koren, M Hossain, I Veltchev, T Galloway, P Flanagan, C Ma, Fox Chase Cancer Center, Philadelphia, PA

SU-E-T-554 Sunday 3:00:00 PM - 6:00:00 PM Room: Exhibit Hall

Purpose: The goal of this work was to evaluate measured vs. calculated surface dose as a function of PTV-to-skin proximity and calculation matrix voxel size, determine effects on plan quality, and provide parameters and levels of uncertainty for clinical use.

Methods: A right-sided CTV with the lateral border 5mm from the surface was delineated on the CT data of a head and neck phantom. A 5mm PTV was generated except laterally where distances of 0-5mm were used. A 7-field IMRT plan was generated using the Eclipse TPS. Optimization was performed where 95% of the PTV receives the prescription dose using a matrix size of 2mm³. Dose calculations were repeated for grid sizes of 1, 3 and 5mm³. For each plan nine point dose values were obtained just inside the phantom surface, corresponding to a 2cm² grid near the central target region. Nine ultra-thin TLDs were placed on the phantom surface corresponding to the grid. Measured and calculated dose values were compared. Conformality, homogeneity and target coverage were compared.

Results: Surface dose is over-estimated by the TPS by 21 and 8% for 5 and 3mm³ voxels, respectively and accurately predicted for 2mm³ voxels. A voxel size of 1mm³ results in underestimation of 13%. Conformality improves with increasing PTV to skin distance and a CI of unity results for grid sizes of 1-3mm³ between 4 and 4.5mm. Hot spot decreases as the PTV moves away from the surface and falls below 110% at 4mm. Underdosage worsens as the PTV approaches the skin.

Conclusions: For decreasing PTV-to-skin distance with this TPS, isodose conformality decreases, 'hot spot' increases, and target coverage degrades. Surface dose is accurately predicted for a 2mm³ voxel size, while choosing a finer or coarser grid results in underestimation or overestimation, respectively. All of the above appear to hold for VMAT.

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