title: Determination of peripheral underdosage at the lung-tumor interface using Monte Carlo radiation transport calculations.
abstract: Prediction of dose distributions in close proximity to interfaces is difficult. In the context of radiotherapy of lung tumors, this may affect the minimum dose received by lesions and is particularly important when prescribing dose to covering isodoses. The objective of this work is to quantify underdosage in key regions around a hypothetical target using Monte Carlo dose calculation methods, and to develop a factor for clinical estimation of such underdosage. A systematic set of calculations are undertaken using 2 Monte Carlo radiation transport codes (egsnrc and geant4). Discrepancies in dose are determined for a number of parameters, including beam energy, tumor size, field size, and distance from chest wall. Calculations were performed for 1-mm³ regions at proximal, distal, and lateral aspects of a spherical tumor, determined for a 6-MV and a 15-MV photon beam. The simulations indicate regions of tumor underdose at the tumor-lung interface. Results are presented as ratios of the dose at key peripheral regions to the dose at the center of the tumor, a point at which the treatment planning system (TPS) predicts the dose more reliably. Comparison with TPS data (pencil-beam convolution) indicates such underdosage would not have been predicted accurately in the clinic. We define a dose reduction factor (DRF) as the average of the dose in the periphery in the 6 cardinal directions divided by the central dose in the target, the mean of which is 0.97 and 0.95 for a 6-MV and 15-MV beam, respectively. The DRF can assist clinicians in the estimation of the magnitude of potential discrepancies between prescribed and delivered dose distributions as a function of tumor size and location. Calculation for a systematic set of "generic" tumors allows application to many classes of patient case, and is particularly useful for interpreting clinical trial data.
authors: School of Applied Sciences, College of Science, Engineering and Health, RMIT University, Melbourne, Victoria, Australia. michael.taylor@rmit.edu.au
authors: Michael
authors: M
authors: Taylor
authors: Leon
authors: L
authors: Dunn
authors: Tomas
authors: T
authors: Kron
authors: Felicity
authors: F
authors: Height
authors: Rick
authors: R
authors: Franich
mesh_terms: D001822:Body Burden; D003198:Computer Simulation; D006801:Humans; D008175:Lung Neoplasms / Q000532:radiotherapy*; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D009928:Organ Specificity; D011874:Radiometry / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D012984:Software